Snapshot img { margin: 10px !important; } AI Data Labeling Market Size 2025-2029

The ai data labeling market size is forecast to increase by USD 1.4 billion, at a CAGR of 21.1% between 2024 and 2029.

The escalating adoption of artificial intelligence and machine learning technologies is a primary driver for the global ai data labeling market. As organizations integrate ai into operations, the need for high-quality, accurately labeled training data for supervised learning algorithms and deep neural networks expands. This creates a growing demand for data annotation services across various data types. The emergence of automated and semi-automated labeling tools, including ai content creation tool and data labeling and annotation tools, represents a significant trend, enhancing efficiency and scalability for ai data management. The use of an ai speech to text tool further refines audio data processing, making annotation more precise for complex applications.Maintaining data quality and consistency remains a paramount challenge. Inconsistent or erroneous labels can lead to flawed model performance, biased outcomes, and operational failures, undermining AI development efforts that rely on ai training dataset resources. This issue is magnified by the subjective nature of some annotation tasks and the varying skill levels of annotators. For generative artificial intelligence (AI) applications, ensuring the integrity of the initial data is crucial. This landscape necessitates robust quality assurance protocols to support systems like autonomous ai and advanced computer vision systems, which depend on flawless ground truth data for safe and effective operation.

What will be the Size of the AI Data Labeling Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019 - 2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe global ai data labeling market's evolution is shaped by the need for high-quality data for ai training. This involves processes like data curation process and bias detection to ensure reliable supervised learning algorithms. The demand for scalable data annotation solutions is met through a combination of automated labeling tools and human-in-the-loop validation, which is critical for complex tasks involving multimodal data processing.Technological advancements are central to market dynamics, with a strong focus on improving ai model performance through better training data. The use of data labeling and annotation tools, including those for 3d computer vision and point-cloud data annotation, is becoming standard. Data-centric ai approaches are gaining traction, emphasizing the importance of expert-level annotations and domain-specific expertise, particularly in fields requiring specialized knowledge such as medical image annotation.Applications in sectors like autonomous vehicles drive the need for precise annotation for natural language processing and computer vision systems. This includes intricate tasks like object tracking and semantic segmentation of lidar point clouds. Consequently, ensuring data quality control and annotation consistency is crucial. Secure data labeling workflows that adhere to gdpr compliance and hipaa compliance are also essential for handling sensitive information.

How is this AI Data Labeling Industry segmented?

The ai data labeling industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments. TypeTextVideoImageAudio or speechMethodManualSemi-supervisedAutomaticEnd-userIT and technologyAutomotiveHealthcareOthersGeographyNorth AmericaUSCanadaMexicoAPACChinaIndiaJapanSouth KoreaAustraliaIndonesiaEuropeGermanyUKFranceItalySpainThe NetherlandsSouth AmericaBrazilArgentinaColombiaMiddle East and AfricaUAESouth AfricaTurkeyRest of World (ROW)

By Type Insights

The text segment is estimated to witness significant growth during the forecast period.The text segment is a foundational component of the global ai data labeling market, crucial for training natural language processing models. This process involves annotating text with attributes such as sentiment, entities, and categories, which enables AI to interpret and generate human language. The growing adoption of NLP in applications like chatbots, virtual assistants, and large language models is a key driver. The complexity of text data labeling requires human expertise to capture linguistic nuances, necessitating robust quality control to ensure data accuracy. The market for services catering to the South America region is expected to constitute 7.56% of the total opportunity.The demand for high-quality text annotation is fueled by the need for ai models to understand user intent in customer service automation and identify critical

These images and associated binary labels were collected from collaborators across multiple universities to serve as a diverse representation of biomedical images of vessel structures, for use in the training and validation of machine learning tools for vessel segmentation. The dataset contains images from a variety of imaging modalities, at different resolutions, using difference sources of contrast and featuring different organs/ pathologies. This data was use to train, test and validated a foundational model for 3D vessel segmentation, tUbeNet, which can be found on github. The paper descripting the training and validation of the model can be found here. Filenames are structured as follows: Data - [Modality]_[species Organ]_[resolution].tif Labels - [Modality]_[species Organ]_[resolution]_labels.tif Sub-volumes of larger dataset - [Modality]_[species Organ]_subvolume[dimensions in pixels].tif Manual labelling of blood vessels was carried out using Amira (2020.2, Thermo-Fisher, UK). Training data: opticalHREM_murineLiver_2.26x2.26x1.75um.tif: A high resolution episcopic microscopy (HREM) dataset, acquired in house by staining a healthy mouse liver with Eosin B and imaged using a standard HREM protocol. NB: 25% of this image volume was withheld from training, for use as test data. CT_murineTumour_20x20x20um.tif: X-ray microCT images of a microvascular cast, taken from a subcutaneous mouse model of colorectal cancer (acquired in house). NB: 25% of this image volume was withheld from training, for use as test data. RSOM_murineTumour_20x20um.tif: Raster-Scanning Optoacoustic Mesoscopy (RSOM) data from a subcutaneous tumour model (provided by Emma Brown, Bohndiek Group, University of Cambridge). The image data has undergone filtering to reduce the background ​(Brown et al., 2019)​. OCTA_humanRetina_24x24um.tif: retinal angiography data obtained using Optical Coherence Tomography Angiography (OCT-A) (provided by Dr Ranjan Rajendram, Moorfields Eye Hospital). Test data: MRI_porcineLiver_0.9x0.9x5mm.tif: T1-weighted Balanced Turbo Field Echo Magnetic Resonance Imaging (MRI) data from a machine-perfused porcine liver, acquired in-house. Test Data MFHREM_murineTumourLectin_2.76x2.76x2.61um.tif: a subcutaneous colorectal tumour mouse model was imaged in house using Multi-fluorescence HREM in house, with Dylight 647 conjugated lectin staining the vasculature ​(Walsh et al., 2021)​. The image data has been processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB: A sub-volume of 480x480x640 voxels was manually labelled (MFHREM_murineTumourLectin_subvolume480x480x640.tif). MFHREM_murineBrainLectin_0.85x0.85x0.86um.tif: an MF-HREM image of the cortex of a mouse brain, stained with Dylight-647 conjugated lectin, was acquired in house ​(Walsh et al., 2021)​. The image data has been downsampled and processed using an asymmetric deconvolution algorithm described by ​Walsh et al., 2020​. NB: A sub-volume of 1000x1000x99 voxels was manually labelled. This sub-volume is provided at full resolution and without preprocessing (MFHREM_murineBrainLectin_subvol_0.57x0.57x0.86um.tif). 2Photon_murineOlfactoryBulbLectin_0.2x0.46x5.2um.tif: two-photon data of mouse olfactory bulb blood vessels, labelled with sulforhodamine 101, was kindly provided by Yuxin Zhang at the Sensory Circuits and Neurotechnology Lab, the Francis Crick Institute ​(Bosch et al., 2022)​. NB: A sub-volume of 500x500x79 voxel was manually labelled (2Photon_murineOlfactoryBulbLectin_subvolume500x500x79.tif). References: ​​Bosch, C., Ackels, T., Pacureanu, A., Zhang, Y., Peddie, C. J., Berning, M., Rzepka, N., Zdora, M. C., Whiteley, I., Storm, M., Bonnin, A., Rau, C., Margrie, T., Collinson, L., & Schaefer, A. T. (2022). Functional and multiscale 3D structural investigation of brain tissue through correlative in vivo physiology, synchrotron microtomography and volume electron microscopy. Nature Communications 2022 13:1, 13(1), 1–16. https://doi.org/10.1038/s41467-022-30199-6 ​Brown, E., Brunker, J., & Bohndiek, S. E. (2019). Photoacoustic imaging as a tool to probe the tumour microenvironment. DMM Disease Models and Mechanisms, 12(7). https://doi.org/10.1242/DMM.039636 ​Walsh, C., Holroyd, N. A., Finnerty, E., Ryan, S. G., Sweeney, P. W., Shipley, R. J., & Walker-Samuel, S. (2021). Multifluorescence High-Resolution Episcopic Microscopy for 3D Imaging of Adult Murine Organs. Advanced Photonics Research, 2(10), 2100110. https://doi.org/10.1002/ADPR.202100110 ​Walsh, C., Holroyd, N., Shipley, R., & Walker-Samuel, S. (2020). Asymmetric Point Spread Function Estimation and Deconvolution for Serial-Sectioning Block-Face Imaging. Communications in Computer and Information Science, 1248 CCIS, 235–249. https://doi.org/10.1007/978-3-030-52791-4_19 ​ 

3D Point Cloud Labeling for DC Layouts Market Outlook

According to our latest research, the global 3D Point Cloud Labeling for DC Layouts market size reached USD 1.18 billion in 2024, with a robust compound annual growth rate (CAGR) of 16.7% projected through the forecast period. By 2033, the market is anticipated to attain a value of USD 5.16 billion, reflecting the rapid adoption of advanced data visualization and asset management solutions in data centers worldwide. The market’s expansion is fueled by increasing demand for precise digital representations of physical assets, which is essential for optimizing data center (DC) layouts, improving operational efficiency, and supporting the growing complexity of modern data center infrastructures.

A primary growth factor for the 3D Point Cloud Labeling for DC Layouts market is the surge in data center construction and modernization projects globally. As organizations accelerate digital transformation and cloud adoption, the need for sophisticated data center environments is rising. 3D point cloud labeling technology enables highly accurate spatial mapping and annotation of data center layouts, which streamlines design, construction, and ongoing management. This technology supports stakeholders in visualizing and planning space utilization, identifying potential bottlenecks, and ensuring that critical infrastructure is optimally organized. The trend towards hyperscale data centers and edge computing further amplifies the market’s momentum, as these facilities require advanced tools for layout planning and asset tracking to maintain high performance and reliability.

Another significant driver is the growing emphasis on automation and artificial intelligence (AI) in facility management. 3D point cloud labeling tools leverage AI algorithms to automate the identification, classification, and tracking of assets within data centers. This automation reduces manual labor, minimizes errors, and enhances security by providing real-time visibility into asset locations and statuses. As data centers become more complex and house increasingly diverse IT equipment, automated point cloud labeling becomes indispensable for maintaining operational continuity, supporting predictive maintenance, and ensuring regulatory compliance. The integration of these tools with building information modeling (BIM) and digital twin technologies is also accelerating market growth by enabling seamless data exchange and holistic facility management.

Furthermore, the market is benefitting from heightened security and surveillance requirements in data center environments. With cyber and physical threats on the rise, data center operators are seeking advanced solutions that offer comprehensive monitoring and incident response capabilities. 3D point cloud labeling enhances security by enabling detailed mapping of facility interiors, supporting the deployment of intelligent surveillance systems, and facilitating rapid identification of unauthorized activities. These capabilities are especially valuable in regulated industries such as BFSI and healthcare, where asset protection and compliance with stringent standards are paramount. As a result, the adoption of 3D point cloud labeling solutions is expected to accelerate across a wide range of end-user segments.

From a regional perspective, North America currently leads the 3D Point Cloud Labeling for DC Layouts market, driven by the high concentration of data centers, rapid technological adoption, and significant investments in digital infrastructure. However, Asia Pacific is emerging as a pivotal growth region, fueled by the expansion of cloud services, increasing data center investments, and supportive government initiatives. Europe is also witnessing steady growth, particularly in countries with strong digital economies and a focus on sustainability. The Middle East & Africa and Latin America are gradually catching up, supported by rising demand for digital services and the entry of global cloud providers. Each region presents unique opportunities and challenges, shaping the overall trajectory of the market over the forecast period.

Component Analysis

The Component segment of the 3D Point Cloud Labeling for DC Layouts market is broadly categorized into software and services. Software solutions dominate the market, accounting for the majority of revenue share in 2024. These platforms provide the core functionalities necessar

Robotics Data Labeling Services Market Outlook

As per our latest research, the global Robotics Data Labeling Services market size stood at USD 1.42 billion in 2024. The market is witnessing robust momentum, projected to expand at a CAGR of 20.7% from 2025 to 2033, reaching an estimated USD 9.15 billion by 2033. This surge is primarily driven by the increasing adoption of AI-powered robotics across various industries, where high-quality labeled data is essential for training and deploying advanced machine learning models. The rapid proliferation of automation, coupled with the growing complexity of robotics applications, is fueling demand for precise and scalable data labeling solutions on a global scale.

The primary growth factor for the Robotics Data Labeling Services market is the accelerating integration of artificial intelligence and machine learning algorithms into robotics systems. As robotics technology becomes more sophisticated, the need for accurately labeled data to train these systems is paramount. Companies are increasingly investing in data annotation and labeling services to enhance the performance and reliability of their autonomous robots, whether in manufacturing, healthcare, automotive, or logistics. The complexity of robotics applications, including object detection, environment mapping, and real-time decision-making, mandates high-quality labeled datasets, driving the marketÂ’s expansion.

Another significant factor propelling market growth is the diversification of robotics applications across industries. The rise of autonomous vehicles, industrial robots, service robots, and drones has created an insatiable demand for labeled image, video, and sensor data. As these applications become more mainstream, the volume and variety of data requiring annotation have multiplied. This trend is further amplified by the shift towards Industry 4.0 and the digital transformation of traditional sectors, where robotics plays a central role in operational efficiency and productivity. Data labeling services are thus becoming an integral part of the robotics development lifecycle, supporting innovation and deployment at scale.

Technological advancements in data labeling methodologies, such as the adoption of AI-assisted labeling tools and cloud-based annotation platforms, are also contributing to market growth. These innovations enable faster, more accurate, and cost-effective labeling processes, making it feasible for organizations to handle large-scale data annotation projects. The emergence of specialized labeling services tailored to specific robotics applications, such as sensor fusion for autonomous vehicles or 3D point cloud annotation for industrial robots, is further enhancing the value proposition for end-users. As a result, the market is witnessing increased participation from both established players and new entrants, fostering healthy competition and continuous improvement in service quality.

In the evolving landscape of robotics, Robotics Synthetic Data Services are emerging as a pivotal component in enhancing the capabilities of AI-driven systems. These services provide artificially generated data that mimics real-world scenarios, enabling robotics systems to train and validate their algorithms without the constraints of physical data collection. By leveraging synthetic data, companies can accelerate the development of robotics applications, reduce costs, and improve the robustness of their models. This approach is particularly beneficial in scenarios where real-world data is scarce, expensive, or difficult to obtain, such as in autonomous driving or complex industrial environments. As the demand for more sophisticated and adaptable robotics solutions grows, the role of Robotics Synthetic Data Services is set to expand, offering new opportunities for innovation and efficiency in the market.

From a regional perspective, North America currently dominates the Robotics Data Labeling Services market, accounting for the largest revenue share in 2024. However, Asia Pacific is emerging as the fastest-growing region, driven by rapid industrialization, expanding robotics manufacturing capabilities, and significant investments in AI research and development. Europe also holds a substantial market share, supported by strong regulatory frameworks and a focus on technological innovation. Meanwhile, Latin

Labeling Tools for Warehouse Vision Models Market Outlook

According to our latest research, the global labeling tools for warehouse vision models market size reached USD 1.42 billion in 2024, reflecting a robust expansion driven by the increasing adoption of automation and artificial intelligence in warehouse management. The market is projected to grow at a CAGR of 15.8% from 2025 to 2033, reaching an estimated USD 5.42 billion by 2033. This impressive growth is primarily fueled by the rising need for accurate data annotation to power vision-based AI models, which are critical for optimizing warehouse operations, reducing errors, and enhancing overall productivity.

One of the primary growth factors for the labeling tools for warehouse vision models market is the exponential increase in the deployment of computer vision technologies in warehouses. As warehouses strive to achieve higher efficiency and reduce manual labor, the integration of vision-based systems for tasks such as inventory monitoring, automated sorting, and quality assurance has become paramount. These systems rely heavily on high-quality labeled datasets for training and validation. As a result, demand for advanced labeling tools—capable of handling complex data types such as images, videos, and 3D point clouds—has surged. The proliferation of e-commerce, with its demand for rapid order fulfillment and precise inventory tracking, further amplifies the need for sophisticated annotation solutions that can support the scale and complexity of modern warehouse environments.

Advancements in machine learning and artificial intelligence are also acting as significant catalysts for this market’s growth. As AI models become more sophisticated, the requirement for accurately labeled datasets increases, especially for applications like object detection, automated sorting, and anomaly detection in warehouses. The evolution of labeling tools, incorporating features like AI-assisted annotation, collaborative workflows, and seamless integration with warehouse management systems, is making it easier for organizations to generate large volumes of high-quality training data. Moreover, the shift towards cloud-based labeling platforms is enabling real-time collaboration among distributed teams, accelerating annotation cycles, and reducing operational costs. This technological evolution is creating a favorable environment for both established players and new entrants to innovate and capture market share.

The growing emphasis on quality control and compliance in warehousing is another critical driver. As regulatory standards around product handling, traceability, and safety become more stringent, warehouses are increasingly leveraging vision models for automated inspection and verification. Accurate labeling of visual data is essential for these models to reliably detect defects, mislabeling, or safety hazards. The adoption of labeling tools that support multiple data modalities and offer robust quality assurance features is therefore on the rise. Additionally, the trend towards digital transformation in logistics and supply chain management is encouraging investments in AI-driven warehouse solutions, further propelling the demand for advanced annotation tools.

From a regional perspective, North America currently dominates the labeling tools for warehouse vision models market, accounting for over 38% of the global revenue in 2024. This leadership is attributed to the rapid adoption of automation and AI technologies by leading logistics and e-commerce companies in the United States and Canada. Europe follows closely, with strong demand from advanced manufacturing and retail sectors. The Asia Pacific region is emerging as the fastest-growing market, driven by the expansion of e-commerce and the modernization of supply chain infrastructure in countries like China, India, and Japan. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as digital transformation initiatives gain momentum in these regions.

Product Type Analysis

The product type segment in the labeling tools for warehouse vision models market is broadly categorized into image labeling tools, video labeling tools, 3D point cloud labeling tools, and others. Image labeling tools currently hold the largest market share, as image-based data remains the most prevalent in warehouse vision applications. These tools are widely u

Labeling Tools for Warehouse Vision Models Market Outlook

According to our latest research, the global market size for Labeling Tools for Warehouse Vision Models reached USD 1.21 billion in 2024, with a robust CAGR of 18.7% projected through the forecast period. By 2033, the market is expected to reach USD 5.89 billion, driven by the increasing adoption of AI-powered vision systems in warehouses for automation and efficiency. The market’s growth is primarily fueled by the rapid digital transformation in the logistics and warehousing sectors, where vision models are revolutionizing inventory management, quality control, and automated sorting processes.

One of the most significant growth factors for the Labeling Tools for Warehouse Vision Models Market is the escalating demand for automation across supply chains and distribution centers. As companies strive to enhance operational efficiency and reduce human error, the integration of advanced computer vision models has become essential. These models, however, require vast amounts of accurately labeled data to function optimally. This necessity has led to a surge in demand for sophisticated labeling tools capable of handling diverse data types, such as images, videos, and 3D point clouds. Moreover, the proliferation of e-commerce and omnichannel retailing has put immense pressure on warehouses to process and ship orders faster, further fueling the need for robust labeling solutions that can support rapid model development and deployment.

Another key driver is the evolution of warehouse robotics and autonomous systems. Modern warehouses are increasingly deploying robots and automated guided vehicles (AGVs) that rely on vision models for navigation, object detection, and picking operations. For these systems to perform accurately, high-quality annotated datasets are crucial. The growing complexity and variety of warehouse environments also necessitate labeling tools that can adapt to different use cases, such as detecting damaged goods, monitoring shelf inventory, and facilitating automated sorting. As a result, vendors are innovating their labeling platforms to offer features like collaborative annotation, AI-assisted labeling, and integration with warehouse management systems, all of which are contributing to market growth.

Additionally, the rise of cloud computing and advancements in machine learning infrastructure are accelerating the adoption of labeling tools in the warehouse sector. Cloud-based labeling platforms offer scalability, remote collaboration, and seamless integration with AI training pipelines, making them highly attractive for large enterprises and third-party logistics providers. These solutions enable warehouses to manage vast datasets, ensure data security, and accelerate the development of vision models. Furthermore, regulatory requirements for traceability and quality assurance in industries such as pharmaceuticals and food & beverage are driving warehouses to invest in state-of-the-art vision models, thereby increasing the demand for comprehensive labeling tools.

From a regional perspective, North America currently leads the Labeling Tools for Warehouse Vision Models Market, accounting for the largest market share in 2024. This dominance is attributed to the early adoption of warehouse automation technologies, a strong presence of leading logistics and e-commerce players, and significant investments in AI research and development. The Asia Pacific region is poised for the fastest growth, supported by the rapid expansion of manufacturing and e-commerce sectors in countries like China, India, and Japan. Europe also presents lucrative opportunities due to stringent quality control regulations and growing focus on supply chain digitization. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, driven by increasing investments in logistics infrastructure and digital transformation initiatives.

Product Type Analysis

The Product Type segment of the Labeling Tools for Warehouse Vi

Public imaging datasets are critical for the development and evaluation of automated tools in cancer imaging. Unfortunately, many of the available datasets do not provide annotations of tumors or organs-at-risk, crucial for the assessment of these tools. This is due to the fact that annotation of medical images is time consuming and requires domain expertise. It has been demonstrated that artificial intelligence (AI) based annotation tools can achieve acceptable performance and thus can be used to automate the annotation of large datasets. As part of the effort to enrich the public data available within NCI Imaging Data Commons (IDC) (https://imaging.datacommons.cancer.gov/) [1], we introduce this dataset that consists of such AI-generated annotations for two publicly available medical imaging collections of Computed Tomography (CT) images of the chest. For detailed information concerning this dataset, please refer to our publication here [2].

We use publicly available pre-trained AI tools to enhance CT lung cancer collections that are unlabeled or partially labeled. The first tool is the nnU-Net deep learning framework [3] for volumetric segmentation of organs, where we use a pretrained model (Task D18 using the SegTHOR dataset) for labeling volumetric regions in the image corresponding to the heart, trachea, aorta and esophagus. These are the major organs-at-risk for radiation therapy for lung cancer. We further enhance these annotations by computing 3D shape radiomics features using the pyradiomics package [4]. The second tool is a pretrained model for per-slice automatic labeling of anatomic landmarks and imaged body part regions in axial CT volumes [5].

We focus on enhancing two publicly available collections, the Non-small Cell Lung Cancer Radiomics (NSCLC-Radiomics collection) [6,7], and the National Lung Screening Trial (NLST collection) [8,9]. The CT data for these collections are available both in The Cancer Imaging Archive (TCIA) [10] and in NCI Imaging Data Commons (IDC). Further, the NSLSC-Radiomics collection includes expert-generated manual annotations of several chest organs, allowing us to quantify performance of the AI tools in that subset of data.

IDC is relying on the DICOM standard to achieve FAIR [10] sharing of data and interoperability. Generated annotations are saved as DICOM Segmentation objects (volumetric segmentations of regions of interest) created using the dcmqi [12], and DICOM Structured Report (SR) objects (per-slice annotations of the body part imaged, anatomical landmarks and radiomics features) created using dcmqi and highdicom [13]. 3D shape radiomics features and corresponding DICOM SR objects are also provided for the manual segmentations available in the NSCLC-Radiomics collection.

The dataset is available in IDC, and is accompanied by our publication here [2]. This pre-print details how the data were generated, and how the resulting DICOM objects can be interpreted and used in tools. Additionally, for further information about how to interact with and explore the dataset, please refer to our repository and accompanying Google Colaboratory notebook.

The annotations are organized as follows. For NSCLC-Radiomics, three nnU-Net models were evaluated ('2d-tta', '3d_lowres-tta' and '3d_fullres-tta'). Within each folder, the PatientID and the StudyInstanceUID are subdirectories, and within this the DICOM Segmentation object and the DICOM SR for the 3D shape features are stored. A separate directory for the DICOM SR body part regression regions ('sr_regions') and landmarks ('sr_landmarks') are also provided with the same folder structure as above. Lastly, the DICOM SR for the existing manual annotations are provided in the 'sr_gt' directory. For NSCLC-Radiomics, each patient has a single StudyInstanceUID. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.

nsclc

2d-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

3d_lowres-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

3d_fullres-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

sr_regions

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_regions_SR.dcm

sr_landmarks

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_landmarks_SR.dcm

sr_gt

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_features_SR.dcm

For NLST, the '3d_fullres-tta' model was evaluated. The data is organized the same as above, where within each folder the PatientID and the StudyInstanceUID are subdirectories. For the NLST collection, it is possible that some patients have more than one StudyInstanceUID subdirectory. A separate directory for the DICOM SR body par regions ('sr_regions') and landmarks ('sr_landmarks') are also provided. The DICOM Segmentation and SR objects are named according to the SeriesInstanceUID of the original CT files.

nlst

3d_fullres-tta

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_SEG.dcm

ReferencedSeriesInstanceUID_features_SR.dcm

sr_regions

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_regions_SR.dcm

sr_landmarks

PatientID

StudyInstanceUID

ReferencedSeriesInstanceUID_landmarks_SR.dcm

The query used for NSCLC-Radiomics is here, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is here. The query used for NLST is here, and a list of corresponding SeriesInstanceUIDs (along with PatientIDs and StudyInstanceUIDs) is here. The two csv files that describe the series analyzed, nsclc_series_analyzed.csv and nlst_series_analyzed.csv, are also available as uploads to this repository.

Version updates:

Version 2: For the regions SR and landmarks SR, changed to use a distinct TrackingUniqueIdentifier for each MeasurementGroup. Also instead of using TargetRegion, changed to use FindingSite. Additionally for the landmarks SR, the TopographicalModifier was made a child of FindingSite instead of a sibling.

Version 3: Added the two csv files that describe which series were analyzed

Version 4: Modified the landmarks SR as the TopographicalModifier for the Kidney landmark (bottom) does not describe the landmark correctly. The Kidney landmark is the "first slice where both kidneys can be seen well." Instead, removed the use of the TopographicalModifier for that landmark. For the features SR, modified the units code for the Flatness and Elongation, as we incorrectly used mm units instead of no units.

Annotation Tools for Robotics Perception Market Outlook

As per our latest research, the global Annotation Tools for Robotics Perception market size reached USD 1.47 billion in 2024, with a robust growth trajectory driven by the rapid adoption of robotics in various sectors. The market is expected to expand at a CAGR of 18.2% during the forecast period, reaching USD 6.13 billion by 2033. This significant growth is attributed primarily to the increasing demand for sophisticated perception systems in robotics, which rely heavily on high-quality annotated data to enable advanced machine learning and artificial intelligence functionalities.

A key growth factor for the Annotation Tools for Robotics Perception market is the surging deployment of autonomous systems across industries such as automotive, manufacturing, and healthcare. The proliferation of autonomous vehicles and industrial robots has created an unprecedented need for comprehensive datasets that accurately represent real-world environments. These datasets require meticulous annotation, including labeling of images, videos, and sensor data, to train perception algorithms for tasks such as object detection, tracking, and scene understanding. The complexity and diversity of environments in which these robots operate necessitate advanced annotation tools capable of handling multi-modal data, thus fueling the demand for innovative solutions in this market.

Another significant driver is the continuous evolution of machine learning and deep learning algorithms, which require vast quantities of annotated data to achieve high accuracy and reliability. As robotics applications become increasingly sophisticated, the need for precise and context-rich annotations grows. This has led to the emergence of specialized annotation tools that support a variety of data types, including 3D point clouds and multi-sensor fusion data. Moreover, the integration of artificial intelligence within annotation tools themselves is enhancing the efficiency and scalability of the annotation process, enabling organizations to manage large-scale projects with reduced manual intervention and improved quality control.

The growing emphasis on safety, compliance, and operational efficiency in sectors such as healthcare and aerospace & defense further accelerates the adoption of annotation tools for robotics perception. Regulatory requirements and industry standards mandate rigorous validation of robotic perception systems, which can only be achieved through extensive and accurate data annotation. Additionally, the rise of collaborative robotics (cobots) in manufacturing and agriculture is driving the need for annotation tools that can handle diverse and dynamic environments. These factors, combined with the increasing accessibility of cloud-based annotation platforms, are expanding the reach of these tools to organizations of all sizes and across geographies.

In this context, Automated Ultrastructure Annotation Software is gaining traction as a pivotal tool in enhancing the efficiency and precision of data labeling processes. This software leverages advanced algorithms and machine learning techniques to automate the annotation of complex ultrastructural data, which is particularly beneficial in fields requiring high-resolution imaging and detailed analysis, such as biomedical research and materials science. By automating the annotation process, this software not only reduces the time and labor involved but also minimizes human error, leading to more consistent and reliable datasets. As the demand for high-quality annotated data continues to rise across various industries, the integration of such automated solutions is becoming increasingly essential for organizations aiming to maintain competitive advantage and operational efficiency.

From a regional perspective, North America currently holds the largest share of the Annotation Tools for Robotics Perception market, accounting for approximately 38% of global revenue in 2024. This dominance is attributed to the regionÂ’s strong presence of robotics technology developers, advanced research institutions, and early adoption across automotive and manufacturing sectors. Asia Pacific follows closely, fueled by rapid industrialization, government initiatives supporting automation, and the presence of major automotiv

Automotive Data Labeling Services Market Outlook

According to our latest research, the global automotive data labeling services market size reached USD 1.49 billion in 2024. The market is demonstrating robust growth, propelled by the escalating integration of artificial intelligence and machine learning in the automotive sector. The market is projected to witness a CAGR of 21.3% from 2025 to 2033, with the total market value forecasted to reach USD 9.85 billion by 2033. The primary growth factor is the surging demand for high-quality labeled data to train advanced driver-assistance systems (ADAS) and autonomous driving algorithms, reflecting a transformative shift in the automotive industry.

The burgeoning adoption of autonomous vehicles and intelligent transportation systems is a significant driver fueling the growth of the automotive data labeling services market. As automotive manufacturers and technology providers race to develop reliable self-driving solutions, the requirement for accurately annotated data has become paramount. Labeled data serves as the backbone for training machine learning models, enabling vehicles to recognize objects, interpret traffic signals, and make real-time decisions. The increasing complexity of automotive systems, including multi-sensor fusion and advanced perception modules, necessitates high volumes of meticulously labeled data across image, video, and sensor modalities. This trend is compelling automotive stakeholders to invest heavily in data labeling services, thereby accelerating market expansion.

Another critical growth factor is the rapid evolution of connected vehicles and the proliferation of advanced driver assistance systems (ADAS). With the automotive industry embracing connectivity, vehicles are generating unprecedented amounts of data from cameras, LiDAR, radar, and other sensors. The need to annotate this data for applications such as lane departure warning, collision avoidance, and adaptive cruise control is intensifying. Moreover, regulatory mandates for safety and the push towards zero-accident mobility are driving OEMs and suppliers to enhance the accuracy and robustness of their perception systems. This, in turn, is boosting the demand for comprehensive data labeling solutions tailored to automotive requirements, further propelling market growth.

The increasing collaboration between automotive OEMs, technology companies, and specialized data labeling service providers is also shaping the market landscape. Partnerships are being formed to leverage domain expertise, ensure data security, and achieve scalability in annotation projects. The emergence of new labeling techniques, such as 3D point cloud annotation and semantic segmentation, is enhancing the quality of training datasets, thereby improving the performance of AI-driven automotive applications. Additionally, the integration of automated and semi-automated labeling tools is reducing annotation time and costs, making data labeling more accessible to a broader range of industry participants. These collaborative efforts and technological advancements are fostering innovation and driving sustained growth in the automotive data labeling services market.

From a regional perspective, North America and Asia Pacific are emerging as the dominant markets for automotive data labeling services. North America, led by the United States, is witnessing significant investments in autonomous driving research and development, while Asia Pacific is experiencing rapid growth due to the expansion of automotive manufacturing hubs and the increasing adoption of smart mobility solutions. Europe, with its strong automotive heritage and regulatory focus on vehicle safety, is also contributing substantially to market growth. The Middle East & Africa and Latin America, though smaller in market share, are gradually recognizing the potential of data-driven automotive technologies, setting the stage for future expansion in these regions.

Service Type Analysis

The service type se

The proposed dataset, termed PC-Urban (Urban Point Cloud), is captured with an Ouster LiDAR sensor with 64 channels. The sensor is installed on an SUV that drives through the downtown of Perth, Western Australia (WA), Australia. The dataset comprises over 4.3 billion points captured for 66K sensor frames. The labelled data is organized as registered and raw point cloud frames, where the former has a different number of registered consecutive frames. We provide 25 class labels in the dataset covering 23 million points and 5K instances. Labelling is performed with PC-Annotate and can easily be extended by the end-users employing the same tool.The data is organized into unlabelled and labelled 3D point clouds. The unlabelled data is provided in .PCAP file format, which is the direct output format of the used Ouster LiDAR sensor. Raw frames are extracted from the recorded .PCAP files in the form of Ply and Excel files using the Ouster Studio Software. Labelled 3D point cloud data consists of registered or raw point clouds. A labelled point cloud is a combination of Ply, Excel, Labels and Summary files. A point cloud in Ply file contains X, Y, Z values along with color information. An Excel file contains X, Y, Z values, Intensity, Reflectivity, Ring, Noise, and Range of each point. These attributes can be useful in semantic segmentation using deep learning algorithms. The Label and Label Summary files have been explained in the previous section. Our one GB raw data contains nearly 1,300 raw frames, whereas 66,425 frames are provided in the dataset, each comprising 65,536 points. Hence, 4.3 billion points captured with the Ouster LiDAR sensor are provided. Annotation of 25 general outdoor classes is provided, which include car, building, bridge, tree, road, letterbox, traffic signal, light-pole, rubbish bin, cycles, motorcycle, truck, bus, bushes, road sign board, advertising board, road divider, road lane, pedestrians, side-path, wall, bus stop, water, zebra-crossing, and background. With the released data, a total of 143 scenes are annotated which include both raw and registered frames.

Annotation Tools for Robotics Perception Market Outlook

According to our latest research, the global Annotation Tools for Robotics Perception market size reached USD 1.36 billion in 2024 and is projected to grow at a robust CAGR of 17.4% from 2025 to 2033, achieving a forecasted market size of USD 5.09 billion by 2033. This significant growth is primarily fueled by the rapid expansion of robotics across sectors such as automotive, industrial automation, and healthcare, where precise data annotation is critical for machine learning and perception systems.

The surge in adoption of artificial intelligence and machine learning within robotics is a major growth driver for the Annotation Tools for Robotics Perception market. As robots become more advanced and are required to perform complex tasks in dynamic environments, the need for high-quality annotated datasets increases exponentially. Annotation tools enable the labeling of images, videos, and sensor data, which are essential for training perception algorithms that empower robots to detect objects, understand scenes, and make autonomous decisions. The proliferation of autonomous vehicles, drones, and collaborative robots in manufacturing and logistics has further intensified the demand for robust and scalable annotation solutions, making this segment a cornerstone in the advancement of intelligent robotics.

Another key factor propelling market growth is the evolution and diversification of annotation types, such as 3D point cloud and sensor fusion annotation. These advanced annotation techniques are crucial for next-generation robotics applications, particularly in scenarios requiring spatial awareness and multi-sensor integration. The shift towards multi-modal perception, where robots rely on a combination of visual, LiDAR, radar, and other sensor data, necessitates sophisticated annotation frameworks. This trend is particularly evident in industries like automotive, where autonomous driving systems depend on meticulously labeled datasets to achieve high levels of safety and reliability. Additionally, the growing emphasis on edge computing and real-time data processing is prompting the development of annotation tools that are both efficient and compatible with on-device learning paradigms.

Furthermore, the increasing integration of annotation tools within cloud-based platforms is streamlining collaboration and scalability for enterprises. Cloud deployment offers advantages such as centralized data management, seamless updates, and the ability to leverage distributed workforces for large-scale annotation projects. This is particularly beneficial for global organizations managing extensive robotics deployments across multiple geographies. The rise of annotation-as-a-service models and the incorporation of AI-driven automation in labeling processes are also reducing manual effort and improving annotation accuracy. As a result, businesses are able to accelerate the training cycles of their robotics perception systems, driving faster innovation and deployment of intelligent robots across diverse applications.

From a regional perspective, North America continues to lead the Annotation Tools for Robotics Perception market, driven by substantial investments in autonomous technologies and a strong ecosystem of AI startups and research institutions. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid industrialization, government initiatives supporting robotics, and increasing adoption of automation in manufacturing and agriculture. Europe also remains a significant market, particularly in automotive and industrial robotics, thanks to stringent safety standards and a strong focus on technological innovation. Collectively, these regional dynamics are shaping the competitive landscape and driving the global expansion of annotation tools tailored for robotics perception.

Component Analysis

The Annotation Tools for Robotics Perception market, when segmented by component, is primarily divided into software and services. Software solutions dominate the market, accounting for the largest revenue share in 2024. This dominance is attributed to the proliferation of robust annotation platforms that offer advanced features such as automated labeling, AI-assisted annotation, and integration with machine learning pipelines. These software tools are designed to handle diverse data types, including images, videos, and 3D point clouds, enabling organizations to efficiently annotate large datasets required for training r

Data Annotation Platforms for Computer Vision Market Outlook

According to our latest research, the global data annotation platforms for computer vision market size reached USD 1.98 billion in 2024, reflecting robust adoption across industries. The market is projected to grow at a CAGR of 25.7% from 2025 to 2033, reaching an estimated USD 14.25 billion by 2033. This exceptional growth is driven by the increasing integration of artificial intelligence (AI) and machine learning (ML) in various sectors, requiring high-quality annotated datasets to train computer vision models. The proliferation of AI-powered applications in industries such as automotive, healthcare, retail, and agriculture is a major catalyst fueling this market’s expansion, as per our latest research findings.

One of the primary growth factors for the data annotation platforms for computer vision market is the escalating demand for accurate and reliable labeled data to power AI and ML algorithms. As organizations across the globe invest heavily in computer vision technologies for applications ranging from autonomous vehicles and facial recognition to medical imaging and smart retail, the need for precise data annotation has become indispensable. The surge in unstructured data, especially images and videos, necessitates robust annotation tools and services to transform raw data into actionable insights. Furthermore, advancements in deep learning architectures have heightened the need for large-scale, meticulously labeled datasets, driving organizations to seek sophisticated annotation platforms that can support complex annotation tasks with high efficiency and scalability.

Another significant driver is the growing adoption of automation and cloud-based solutions within data annotation platforms. Automation, powered by AI-assisted annotation and active learning, is helping enterprises reduce manual labor, accelerate project timelines, and minimize human error. Cloud-based deployment models, meanwhile, offer flexibility, scalability, and remote accessibility, making it easier for organizations to handle large annotation projects distributed across multiple locations. These technological advancements are not only enhancing the speed and accuracy of data annotation processes but are also lowering entry barriers for small and medium-sized enterprises (SMEs) seeking to leverage computer vision capabilities without investing heavily in infrastructure or skilled labor.

The rising focus on data privacy and regulatory compliance is also shaping the trajectory of the data annotation platforms for computer vision market. Industries such as healthcare and finance, which handle sensitive personal and financial information, are increasingly seeking annotation solutions that ensure data security and adherence to regional regulations like GDPR and HIPAA. This has led to the emergence of specialized annotation platforms equipped with robust security features, audit trails, and compliance certifications. As regulatory landscapes evolve and data sovereignty concerns intensify, the demand for compliant and secure annotation platforms is expected to witness substantial growth, further propelling market expansion.

From a regional perspective, North America currently dominates the data annotation platforms for computer vision market, owing to its early adoption of AI technologies, presence of leading tech companies, and significant investments in research and development. However, the Asia Pacific region is anticipated to exhibit the fastest growth over the forecast period, fueled by rapid digital transformation, burgeoning AI start-up ecosystems, and increasing government initiatives to promote AI and machine learning adoption. Europe also holds a considerable market share, driven by stringent data privacy regulations and a strong focus on industrial automation. Latin America and the Middle East & Africa are gradually emerging as promising markets, supported by growing awareness and investment in AI-driven applications across various sectors.

Component Analysis

The data annotation platforms for computer vision market is segmented by component into software and services, each playing a crucial role in addressing diverse industry requirements. The software segment encompasses a wide array of annotation tools and platforms designed to facilitate the labeling of visual data, including images, videos, and 3D point clouds. These platforms often integrate advanced features such as AI-a

Hepatic Vessel Skeletonization Dataset (Task08_HepaticVessel)

Overview

This dataset is derived from the hepatic vessel task of the Medical Segmentation Decathlon (MSD) Task 8. It comprises manually revised vessel skeletons and modified vessel segmentations that are initially generated via automatic 3D thinning and subsequently refined through manual revision. Both the skeletons and the labels have been refined to provide a high-quality ground truth for skeletonization algorithm evaluation.

Dataset Description

Modifications from Original MSD Task 8

Label modifications: Using 3D Slicer, vessel segmentations were refined to remove vessels not located within the liver parenchyma, large segmentations of the inferior vena cava and aorta that were inconsistent across the dataset, and anatomical structures not relevant for hepatic vessel skeletonization analysis.

Skeleton revision: The manual revision process addressed broken and missing branches, incorrect or ambiguous vessel representations, and redundant skeleton points generated by the automatic thinning algorithm.

Dataset Characteristics

The dataset covers various anatomical aspects including vessel representation up to the third level of ramification, anatomically diverse hepatic vessel structures, and consistent spatial resolution and coordinate systems.

Level of ramification is defined as the level of branching in a vessel tree where branches extend at least three levels deep from the main trunk following anatomical hierarchy.

File Structure

Task08_HepaticVessel/├── 0_README.md # This documentation file├── labelsTr/ # Modified vessel segmentations (NIfTI format)│ ├── hepaticvessel_001_mod.nii.gz│ ├── hepaticvessel_002_mod.nii.gz│ ├── hepaticvessel_004_mod.nii.gz│ ├── hepaticvessel_005_mod.nii.gz│ ├── hepaticvessel_007_mod.nii.gz│ ├── hepaticvessel_008_mod.nii.gz│ ├── hepaticvessel_010_mod.nii.gz│ ├── hepaticvessel_011_mod.nii.gz│ ├── hepaticvessel_013_mod.nii.gz│ ├── hepaticvessel_016_mod.nii.gz│ └── hepaticvessel_018_mod.nii.gz└── skeletons/ # Manually revised skeletons (JSON format) ├── hepaticvessel_001_LNC.json ├── hepaticvessel_001_NVO.json ├── hepaticvessel_001_ROF.json ├── hepaticvessel_002_LNC.json ├── hepaticvessel_002_NVO.json ├── hepaticvessel_004_LNC.json ├── hepaticvessel_004_NVO.json ├── hepaticvessel_005_LNC.json ├── hepaticvessel_005_NVO.json ├── hepaticvessel_007_LNC.json ├── hepaticvessel_007_NVO.json ├── hepaticvessel_008_NVO.json ├── hepaticvessel_010_NVO.json ├── hepaticvessel_011_NVO.json ├── hepaticvessel_013_NVO.json ├── hepaticvessel_016_NVO.json └── hepaticvessel_018_LNC.json

Naming Convention

Vessel Segmentations (labelsTr/)

Pattern: hepaticvessel_[3-digit-number]_mod.nii.gzExample: hepaticvessel_008_mod.nii.gz

Skeletons (skeletons/)

Pattern: hepaticvessel_[3-digit-number]_[ANNOTATOR_INITIALS].jsonExample: hepaticvessel_008_LNC.json

Annotator Codes

LNC: Lois Nodar Corral**NVO**: Noelia Velo Outumuro**ROF**: Roque Otero Freiría

Data Format

Vessel Segmentations

Format: NIfTI (.nii.gz)Binary masks (0 = background, 1 = vessel)Coordinate system: RAS (Right-Anterior-Superior)Spatial resolution: Variable (inherited from original MSD dataset)

Skeletons

Format: JSON arrays containing 3D coordinatesCoordinate system: Voxel coordinates (matching corresponding segmentation)Structure:json[ [x1, y1, z1], [x2, y2, z2], [x3, y3, z3], ...]

Each coordinate triplet [x, y, z] represents a voxel position in the 3D volume where the skeleton passes through.

Dataset Statistics

• Total cases: 11 vessel segmentations- Total skeletons: 17 manually revised skeletons- Multiple annotations: Some cases have multiple annotator versions for validation- Coverage: Cases 001, 002, 004, 005, 007, 008, 010, 011, 013, 016, 018

File Correspondence

| Case | Segmentation File | Available Skeletons ||------|-------------------|-------------------|| 001 | hepaticvessel_001_mod.nii.gz | LNC, NVO, ROF || 002 | hepaticvessel_002_mod.nii.gz | LNC, NVO || 004 | hepaticvessel_004_mod.nii.gz | LNC, NVO || 005 | hepaticvessel_005_mod.nii.gz | LNC, NVO || 007 | hepaticvessel_007_mod.nii.gz | LNC, NVO || 008 | hepaticvessel_008_mod.nii.gz | NVO || 010 | hepaticvessel_010_mod.nii.gz | NVO || 011 | hepaticvessel_011_mod.nii.gz | NVO || 013 | hepaticvessel_013_mod.nii.gz | NVO || 016 | hepaticvessel_016_mod.nii.gz | NVO || 018 | hepaticvessel_018_mod.nii.gz | LNC |

Usage Guidelines

1. Correspondence: Each skeleton file corresponds to a vessel segmentation with the same case number2. Coordinate system: Skeleton coordinates are in voxel space of the corresponding NIfTI volume3. Validation: Cases with multiple annotators can be used for inter-observer variability studies4. Citation: Please cite the original Medical Segmentation Decathlon when using this dataset

Software Tools

The manual revision was performed using custom Python tools for skeleton visualization and editing. The annotation software used for skeleton revision is available in a separate GitHub repository (link to be provided upon publication). This tool provides an interactive 3D visualization environment that allows for precise skeleton editing, branch correction, and quality validation.

The Python-based annotation tool, available at https://github.com/Removirt/skeleton-viewer features interactive 3D visualization of vessel segmentations and skeletons, point-by-point skeleton editing capabilities, branch connection and disconnection tools, real-time validation of topological correctness, and multi-platform compatibility (Windows, macOS, Linux).

Dataset Access

This dataset is publicly available through Zenodo. The complete dataset including all vessel segmentations, manually revised skeletons, and documentation can be downloaded from: https://doi.org/10.5281/zenodo.15729285

Citation

This DatasetNodar-Corral, L., Fdez-Gonzalez, M., Fdez-Vidal, X. R., Otero Freiría, R., Velo Outumuro, N., & Comesaña Figueroa, E. (2025). Refined 3D Hepatic Vessel Skeleton Dataset from the Medical Segmentation Decathlon (Task08_HepaticVessel) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.15729285

Original Medical Segmentation DecathlonSimpson, A. L., Antonelli, M., Bakas, S., et al. (2019). A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:1902.09063.

BibTeX Format```bibtex@dataset{hepatic_vessel_skeletons_2025, author = {Nodar-Corral, L., Fdez-Gonzalez, M., Fdez-Vidal, X. R., Otero Freiría, R., Velo Outumuro, N., & Comesaña Figueroa, E.}, title = {Refined 3D Hepatic Vessel Skeleton Dataset from the Medical Segmentation Decathlon (Task08_HepaticVessel)}, year = {2025}, publisher = {Zenodo}, doi = {10.5281/zenodo.15729285}, url =

@article{simpson2019large, title={A large annotated medical image dataset for the development and evaluation of segmentation algorithms}, author={Simpson, Amber L and Antonelli, Michela and Bakas, Spyridon and others}, journal={arXiv preprint arXiv:1902.09063}, year={2019}}```

Contact

For questions about this dataset or annotation methodology, please contact the first author on lois.nodar.corral@usc.es or loisnodar@gmail.com, or any of the other authors on their ORCID correspondence.

Increasing improvements in sensor technologies as well as machine learning methods allow an efficient collection, processing and analysis of the dynamic environment, which can be used for detection and tracking of traffic participants. Current datasets in this domain mostly present a single view, preventing high accurate pose estimations by occlusions. The integration of different, simultaneously acquired data allows to exploit and develop collaboration principles to increase the quality, reliability and integrity of the derived information. This work addresses this problem by providing a multi-view dataset, including 2D image information (videos) and 3D point clouds with labels of the traffic participants in the scene. The dataset was recorded during different weather and light conditions on several days at a large junction in Hanover, Germany. Paper Dataset teaser video: https://youtu.be/elwFdCu5IFo Dataset download path: https://data.uni-hannover.de/vault/ikg/busch/LUMPI/ Labeling process pipeline video: https://youtu.be/Ns6qsHsb06E Python-SDK: https://github.com/St3ff3nBusch/LUMPI-SDK-Python Labeling Tool/ C++ SDK: https://github.com/St3ff3nBusch/LUMPI-Labeling

Abstract

We introduce MedMNIST, a large-scale MNIST-like collection of standardized biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre-processed into 28x28 (2D) or 28x28x28 (3D) with the corresponding classification labels, so that no background knowledge is required for users. Covering primary data modalities in biomedical images, MedMNIST is designed to perform classification on lightweight 2D and 3D images with various data scales (from 100 to 100,000) and diverse tasks (binary/multi-class, ordinal regression and multi-label). The resulting dataset, consisting of approximately 708,000 2D images and 10,000 3D images in total, could support numerous research and educational purposes in biomedical image analysis, computer vision and machine learning. We benchmark several baseline methods on MedMNIST, including 2D / 3D neural networks and open-source / commercial AutoML tools.

The data and code are publicly available at https://medmnist.com/.

Note: This dataset is NOT intended for clinical use.

We recommend our official code to download, parse and use the MedMNIST dataset:

pip install medmnist

Citation

If you find this project useful, please cite both v1 and v2 paper as:

Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, Bingbing Ni. Yang, Jiancheng, et al. "MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification." Scientific Data, 2023.

Jiancheng Yang, Rui Shi, Bingbing Ni. "MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis". IEEE 18th International Symposium on Biomedical Imaging (ISBI), 2021.

or using bibtex:

@article{medmnistv2,
  title={MedMNIST v2-A large-scale lightweight benchmark for 2D and 3D biomedical image classification},
  author={Yang, Jiancheng and Shi, Rui and Wei, Donglai and Liu, Zequan and Zhao, Lin and Ke, Bilian and Pfister, Hanspeter and Ni, Bingbing},
  journal={Scientific Data},
  volume={10},
  number={1},
  pages={41},
  year={2023},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{medmnistv1,
  title={MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis},
  author={Yang, Jiancheng and Shi, Rui and Ni, Bingbing},
  booktitle={IEEE 18th International Symposium on Biomedical Imaging (ISBI)},
  pages={191--195},
  year={2021}
}

Please also cite the corresponding paper(s) of source data if you use any subset of MedMNIST as per the description on the project website.

License

The MedMNIST dataset is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0), except DermaMNIST under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).

The code is under Apache-2.0 License.

Changelog

v2.2 (this repository): We have removed a small number of mistakenly included blank samples in OrganAMNIST, OrganCMNIST, OrganSMNIST, OrganMNIST3D, and VesselMNIST3D.

v2.1: We have fixed the mistake in the file of NoduleMNIST3D (i.e., nodulemnist3d.npz). More details in this issue.

v2.0: Initial repository of MedMNIST v2, add 6 datasets for 3D and 2 for 2D.

v1.0: Initial repository of MedMNIST v1, 10 datasets for 2D.

The Voxel dataset is a constructed dataset of 3D shapes designed to present a unique problem for ML and NAS tools. Instead of a photo of a 3D object, we exploit ML's ability to work across N number of 'colour' channels and use this dimension as a third dimension for images. This dataset is one of the three hidden datasets used by the 2024 NAS Unseen-Data Challenge. The images include 70,000 generated 3D Images of seven different shapes that we generated by creating a 20x20x20 grid of points in 3d space, and randomly generated different 3D shapes (see below) and recorded which of the points the shape collided with, generating the voxel like shapes in the dataset. The data has a shape of (n, 20, 20, 20) where n is the number of samples in the corresponding set (50,000 for training, 10,000 for validation, and 10,000 for testing). For each class (shape), we generated 10,000 samples evenly distributed between the three sets. The three classes and corresponding numerical labels are as follows: Sphere: 0, Cube: 1, Cone: 2, Cylinder: 3, Ellipsoid: 4, Cuboid: 5, Pyramid: 6

NumPy (.npy) files can be opened through the NumPy Python library, using the numpy.load() function by inputting the path to the file into the function as a parameter. The metadata file contains some basic information about the datasets, and can be opened in many text editors such as vim, nano, notepad++, notepad, etc

Organotypic, three dimensional (3D) cell culture models of epithelial tumour types such as prostate cancer recapitulate key aspects of the architecture and histology of solid cancers. Morphometric analysis of multicellular 3D organoids is particularly important when additional components such as the extracellular matrix and tumour microenvironment are included in the model. The complexity of such models has so far limited their successful implementation. There is a great need for automatic, accurate and robust image segmentation tools to facilitate the analysis of such biologically relevant 3D cell culture models. We present a segmentation method based on Markov random fields (MRFs) and illustrate our method using 3D stack image data from an organotypic 3D model of prostate cancer cells co-cultured with cancer-associated fibroblasts (CAFs). The 3D segmentation output suggests that these cell types are in physical contact with each other within the model, which has important implications for tumour biology. Segmentation performance is quantified using ground truth labels and we show how each step of our method increases segmentation accuracy. We provide the ground truth labels along with the image data and code. Using independent image data we show that our segmentation method is also more generally applicable to other types of cellular microscopy and not only limited to fluorescence microscopy.

✨ Introduction The SemanticRail3D dataset is a 3D point cloud collection tailored for railway infrastructure semantic and instance segmentation. Originally, the dataset comprises 438 point clouds covering approximately 200 meters of track each—with a total of around 2.8 billion points annotated into 11 semantic classes. Collected using high-resolution LiDAR via a LYNX Mobile Mapper (≈980 points/m² with 5mm precision), this dataset serves as an excellent benchmark for state-of-the-art AI models .

🚀 Key Enhancements & Processing To further enrich its utility for machine learning applications, the dataset has undergone several advanced preprocessing steps and quality assurance measures:

🔍 Data Standardization via PCA Targeted Features: • Linear elements, including rails and all associated wires.

PCA Application: • Extracts the principal orientation of these elements by identifying the axis of maximum variance.

Reorientation: • Aligns the extracted principal axis with the x-axis, ensuring consistency and simplifying downstream analysis.

📸 Multi-Perspective Visualizations Each point cloud in the dataset is accompanied by four rendered images, generated from distinct camera viewpoints to enhance interpretability and usability. These views are designed to showcase the spatial structure of the railway environment from meaningful angles, aiding both visual inspection and AI model training.

The saved camera views are based on spherical coordinates and include:

🔹 Front View • A head-on perspective with a slight downward angle (azimuth = 50°, elevation = 35°) to give a balanced overview of the scene structure.

🔹 Side View • A lateral perspective (azimuth = 130°, elevation = 55°) that highlights the side profile of rail and overhead wire structures.

🔹 Diagonal View • An oblique angle (azimuth = -40°, elevation = 55°) providing depth perception and a richer understanding of the 3D layout.

🔹 Overhead View • A top-down (bird’s-eye) perspective (azimuth = -140°, elevation = 35°) showing the full track arrangement and spatial alignment.

🎨 Visual Color Coding

Color Code Mapping: The points in the images are colorized based on a standardized mapping to clearly differentiate between semantic classes:

🎨 Semantic Class Color Legend

✅ Quality Assurance through Human Evaluation

Detailed Review: • Each point cloud undergoes a rigorous expert review to ensure accurate and consistent labeling.

Rating System: • Files are rated on a scale from 1 (needs improvement) to 5 (excellent quality). • The ratings are compiled in a separate CSV file for ease of reference.

Label Error Codes: Within the CSV file, objects with labeling mistakes are flagged using the following codes: • R: Rails • W: Any kind of wires and cables • M: Masts • TS: Traffic signs • Noise: Miscellaneous errors or irrelevant data

🎯 Dataset Highlights Comprehensive Coverage: • 438 point clouds covering ~200 meters each • Approximately 2.8 billion points annotated into 11 semantic classes

High-Quality LiDAR Acquisition: • Dual LiDAR sensors on a Mobile Mapping System • Point density of ~980 points/m² and a precision of 5 mm

Consistent Data Alignment: • PCA is applied to linear elements (rails and wires) for reorientation along the x-axis

Enhanced Visualizations: • Four images per point cloud provide multiple viewpoints • Points are colorized based on the standardized color code for immediate visual clarity

Robust Quality Control: • Expert human evaluation rates each point cloud (1 to 5) • A separate CSV file holds the quality ratings along with detailed error codes for any mislabeling

🔗 Summary The enhanced SemanticRail3D dataset builds on a robust collection of 3D railway point clouds with advanced preprocessing techniques and comprehensive quality assurance. Through PCA-driven alignment, multi-perspective image generation, and an intuitive color coding system, the dataset standardizes data for efficient model training. Furthermore, the additional CSV file detailing human evaluation ratings and specific label error codes provides users with clear insights into the reliability and accuracy of the annotations. This complete solution sets a new benchmark for railway infrastructure analysis, empowering researchers and practitioners to develop more precise and reliable AI solutions.

3DHBD 3D Humanix Blender Dataset for Student pose detection applications.

1.PUBLICATION<

2024 3DHBD: Synthetic 3D Dataset for Advanced Student Behavior Analysis in Educational Environments

Journal: Balochistan Journal of Engineering & Applied Sciences (BJEAS)

Status: Published [Paper Link]

2.PUBLICATION<

2024

Advanced Student Behavior Analysis Using Dual-Model Approach for Pose and Emotion Detection

Journal: Multimedia Tools and Applications by Springer

Status: Under review

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Overview: 3DHBD (3D Humanix Blender Dataset) is a high-quality synthetic dataset developed using Blender, an open-source and freely accessible software. Due to privacy and security concerns surrounding student data, suitable datasets for student pose detection are scarce. 3DHBD addresses this gap by providing a comprehensive dataset aimed at detection of abnormal behaviour of students in crowded educational environments.

Author Introduction: This dataset was created to fulfill the thesis requirements for a master's degree. This project was created by Hamza Iqbal, [ Linkedin, Github ] who completed his Master's Degree in Electrical Engineering (Signal & Image Processing) from the prestigious Institute of Space Technology (IST), Islamabad, Pakistan in July 2024. Hamza previously holds Bachelor's Degree in Electrical Engineering (Electronics) from Bahria University, Islamabad.

He worked under the supervision of Dr. Madiha Tahir, an Assistant Professor at IST. Dr. Madiha’s research interests lie in the image processing and machine learning domains. [Google Scholar ID]

Key Features of the Dataset: 1. Synthetic Generation: All data is synthetic, ensuring that no actual student information is used. This maintains the dataset's privacy and security integrity. 2. Blender-Based: Created with Blender, an open-source software, it guarantees flexibility for researchers and is freely accessible. 3. High-Quality Labels: Precise labeling of student poses ensures reliable and consistent data for training and testing. 4. Diverse Poses: The dataset contains a diverse range of student poses, enabling more robust model training for pose detection. 5. Educational Context: The dataset is specifically curated for educational settings, making it highly relevant for researchers focused on classroom behavior analysis. 6. Robust Supervision: The dataset was developed under the guidance of an experienced faculty member, ensuring high academic standards and data quality.

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