This dataset contains a collection of medical imaging files for use in the "Medical Image Processing with Python" lesson, developed by the Netherlands eScience Center.

The dataset includes:

1. SimpleITK compatible files: MRI T1 and CT scans (training_001_mr_T1.mha, training_001_ct.mha), digital X-ray (digital_xray.dcm in DICOM format), neuroimaging data (A1_grayT1.nrrd, A1_grayT2.nrrd). Data have been downloaded from here.
2. MRI data: a T2-weighted image (OBJECT_phantom_T2W_TSE_Cor_14_1.nii in NIfTI-1 format). Data have been downloaded from here.
3. Example images for the machine learning lesson: chest X-rays (rotatechest.png, other_op.png), cardiomegaly example (cardiomegaly_cc0.png).
4. Additional anonymized data: TBA

These files represent various medical imaging modalities and formats commonly used in clinical research and practice. They are intended for educational purposes, allowing students to practice image processing techniques, machine learning applications, and statistical analysis of medical images using Python libraries such as scikit-image, pydicom, and SimpleITK.

brain MRI

Medical Imaging

## Overview

Medical Imaging is a dataset for object detection tasks - it contains Thyroid Nodules annotations for 1,079 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Access detailed MRI datasets of various body parts. Perfect for advancing medical research, AI development, and diagnostic technology innovation.

🧠 About This OTS Dataset

Step into our specialized Medical Image Collection showcasing MRI scans of different parts of the body. This meticulously curated dataset is designed to support medical imaging research, model training, diagnostic system development, and clinical analysis.

  📊 Metadata Availability: Insights into Participant Details

While textual transcriptions are not included, each image is enriched with comprehensive metadata, offering:

Patient demographics… See the full description on the dataset page: https://huggingface.co/datasets/Macgence/medical-imaging-datasets-for-mri-of-different-parts-of-body.

Medical Image

## Overview

Medical Image is a dataset for object detection tasks - it contains Mask Glove Vial Syringe Spluit I annotations for 2,908 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Introduction

Medical Imaging Statistics: Medical imaging is essential for visualizing internal body structures to aid in diagnosis, monitoring, and treatment.

Key modalities include X-ray imaging, which highlights bones and some soft tissues; and computed tomography (CT).

Which provides cross-sectional images for detecting fractures and tumors; Teleradiology (MRI). It is known for its detailed soft tissue images; and ultrasound, widely used in obstetrics and cardiology.

Nuclear medicine employs radioactive materials to evaluate organ function, while positron emission tomography (PET) helps assess metabolic activity in cancer.

Recent advancements, such as hybrid imaging techniques and the integration of artificial intelligence. Further, enhances diagnostic accuracy and treatment efficacy, solidifying the role of medical imaging in modern healthcare.

Transform healthcare diagnostics with image segmentation. Dive into advanced techniques for detailed medical imaging, aiding patient care.

Background

The COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.

Purpose

To create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. The Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD) collection of COVID-related imaging datasets and expert annotations to support research and education. RICORD data will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Materials and Methods

This dataset was a collaboration between the RSNA and Society of Thoracic Radiology (STR).

Results

The RSNA International COVID-19 Open Annotated Radiology Database (RICORD) release 1b consists of 120 thoracic computed tomography (CT) scans of COVID negative patients from four international sites.

Patient Selection: Patients at least 18 years in age receiving negative diagnosis for COVID-19.

Data Abstract

1. 120 de-identified Thoracic CT scans from COVID negative patients.

2. Supporting clinical variables: MRN*, Age, Exam Date/Time*, Exam Description, Sex, Study UID*, Image Count, Modality, Symptomatic, Testing Result, Specimen Source (* pseudonymous values).

Research Benefits

As this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical “ground truth” as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.

Access a specialized MRI dataset for brain cancer research. Ideal for medical studies, AI training, and early disease detection advancements.

Explore a comprehensive dataset of human skin bitten medical images. Designed for research, it supports medical imaging studies and diagnostic advancements.

The Dataset is a meticulously curated high-quality dataset specifically designed for semantic-guided image fusion in the medical domain. This dataset aims to facilitate advanced research and development in multimodal medical image analysis by providing a comprehensive collection of images from various imaging modalities.

🧠 About This OTS Dataset

Step into our specialized Medical Image Collection featuring Glioma Cancer MRI Medical Imaging. This curated dataset is tailored to deepen medical understanding, support diagnostic tool development, and advance AI models trained for glioma cancer detection and classification.

  📊 Metadata Availability: Insights into Participant Details

Although the dataset does not include textual transcriptions, each image is enriched with comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/Macgence/glioma-cancer-mri-medical-imaging-dataset.

The dataset contains multi-modal data from over 85,000 open access and de-identified case reports, including metadata, clinical cases, image captions and more than 160,000 images. Images and clinical cases belong to different medical specialties, such as oncology, cardiology, surgery and pathology. The structure of the dataset allows to easily map images with their corresponding article metadata, clinical case, captions and image labels. Details of the data structure can be found in the file data_dictionary.csv.

More than 110,000 patients and 300,000 medical doctors and researchers were involved in the creation of the articles included in this dataset. The citation data of each article can be found in the metadata.parquet file.

Refer to the examples showcased in this GitHub repository to understand how to optimize the use of this dataset.

The Medical Image Management Market Report is Segmented by System (Picture Archiving and Communications System (PACS), Vendor-Neutral Archive (VNA), Application-Independent Clinical Archive (AICA), and More), Deployment Mode (On-Premise, Cloud-Based and Hybrid), End User (Hospitals, Diagnostic Imaging Centers, and More), and Geography (North America, Europe, and More). The Market Forecasts are Provided in Terms of Value (USD).

Sparsity of annotated data is a major limitation in medical image processing tasks such as registration. Registered multimodal image data are essential for the diagnosis of medical conditions and the success of interventional medical procedures. To overcome the shortage of data, we present a method that allows the generation of annotated multimodal 4D datasets. We use a CycleGAN network architecture to generate multimodal synthetic data from the 4D extended cardiac–torso (XCAT) phantom and real patient data. Organ masks are provided by the XCAT phantom; therefore, the generated dataset can serve as ground truth for image segmentation and registration. Compared to real patient data, the synthetic data showed good agreement regarding the image voxel intensity distribution and the noise characteristics. The generated T1-weighted magnetic resonance imaging, computed tomography (CT), and cone beam CT images are inherently co-registered.

The Medical Imaging Software Market report segments the industry into By Imaging Type (2D Imaging, 3D Imaging, 4D Imaging), By Application (Dental Applications, Orthopaedic Applications, Cardiology Applications, Obstetrics and Gynaecology Applications, Mammography Applications, Urology and Nephrology Applications, Other Applications), and Geography (North America, Europe, Asia-Pacific, Rest of the World).

The Scottish Medical Imaging (SMI) Service has created a database of deidentified images (for example CT Scans, MRIs) that can be used by researchers who require access to clinical images and metadata for their approved research. This new national resource will be used to provision de-identified images and associated report data to researchers which, if required, may be linked to other available de-identified datasets

AI Medical Imaging Solution Market Outlook

The global AI medical imaging solution market size in 2023 is estimated to be $2.5 billion and is projected to reach $15.7 billion by 2032, exhibiting a compound annual growth rate (CAGR) of 22.3%. This significant growth can be attributed to various factors, including advancements in artificial intelligence, increased adoption of AI technologies in healthcare, and the rising demand for accurate and efficient diagnostic solutions.

One of the primary growth factors driving the AI medical imaging solution market is the increasing prevalence of chronic diseases such as cancer, cardiovascular diseases, and neurological disorders. These conditions necessitate advanced diagnostic tools for early detection and effective treatment planning. AI medical imaging solutions offer enhanced accuracy and efficiency compared to traditional imaging techniques, enabling healthcare providers to make more informed decisions and improve patient outcomes. Moreover, the integration of AI with medical imaging facilitates the identification of subtle abnormalities that may be missed by human radiologists, thereby enhancing diagnostic precision.

Another significant factor contributing to market growth is the ongoing technological advancements in AI and machine learning algorithms. The development of sophisticated AI models capable of analyzing large datasets and producing actionable insights has revolutionized the medical imaging landscape. These advancements have led to the creation of AI-powered imaging tools that can automatically detect and classify various medical conditions, significantly reducing the time required for diagnosis and treatment planning. Additionally, continuous improvements in imaging hardware and software are expected to further drive the adoption of AI medical imaging solutions in the coming years.

The growing focus on reducing healthcare costs and improving operational efficiency is also a key driver of market growth. AI medical imaging solutions help healthcare institutions optimize their workflows by automating routine tasks and minimizing the need for manual intervention. This not only reduces the workload of healthcare professionals but also lowers the chances of human error, resulting in more accurate and consistent diagnoses. Moreover, AI solutions can streamline the management of imaging data, enabling faster access to patient information and facilitating more efficient collaboration among healthcare teams.

Radiology AI is becoming increasingly integral to the medical imaging landscape, offering transformative capabilities that enhance diagnostic precision and efficiency. By leveraging advanced algorithms, Radiology AI can analyze vast amounts of imaging data to identify patterns and anomalies that might be overlooked by human eyes. This technology not only supports radiologists in making more accurate diagnoses but also streamlines the workflow by automating routine tasks, thereby allowing healthcare professionals to focus on more complex cases. As the demand for high-quality diagnostic services grows, Radiology AI is poised to play a crucial role in meeting these needs, ultimately leading to improved patient outcomes and more personalized care.

Regionally, North America currently dominates the AI medical imaging solution market, accounting for the largest share due to the high adoption rate of advanced technologies, well-established healthcare infrastructure, and significant investments in research and development. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by increasing healthcare expenditure, growing awareness about AI technologies, and government initiatives to modernize healthcare systems. Europe, Latin America, and the Middle East & Africa are also anticipated to experience steady growth, supported by favorable regulatory frameworks and rising demand for advanced diagnostic solutions.

Component Analysis

The AI medical imaging solution market is segmented by component into software, hardware, and services. The software segment holds the largest market share and is projected to continue its dominance throughout the forecast period. This is primarily due to the increasing demand for AI-powered diagnostic tools that can enhance imaging accuracy and efficiency. AI software solutions are being widely adopted by healthcare providers to streamline diagnostic processes, reduce interpretation time, and improve patient outcomes.

Global AI in Medical Imaging Market size is expected to be worth around US$ 16.88 Billion by 2034 from US$ 1.70 Billion in 2024.