In 1204 CT images we segmented 104 anatomical structures (27 organs, 59 bones, 10 muscles, 8 vessels) covering a majority of relevant classes for most use cases. The CT images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. s0720/segmentations/portal_vein_and_splenic_vein.nii.gz 187.74kB s0720/segmentations/pancreas.nii.gz 45.25kB s0720/segmentations/lung_upper_lobe_right.nii.gz 218.92kB s0720/segmentations/lung_upper_lobe_left.nii.gz 230.82kB s0720/segmentations/lung_middle_lobe_right.nii.gz 201.18kB s0720/segmentations/lung_lower_lobe_right.nii.gz 240.63kB s0720/segmentations/lung_lower_lobe_left.nii.gz 239.49kB s0720/segmentations/liver.nii.gz 273.08kB s0720/segmentations/kidney_right.nii.gz 198.91kB s0720/segmentations/kidney_left.nii.gz 197.82kB s0720/segmentations/inferi

Dataset Card for TotalSegmentator

This is a FiftyOne dataset with 50 samples.

  Installation

If you haven't already, install FiftyOne: pip install -U fiftyone

  Usage

import fiftyone as fo import fiftyone.utils.huggingface as fouh

Load the dataset

Note: other available arguments include 'max_samples', etc

dataset = fouh.load_from_hub("dgural/Total-Segmentator-50")

Launch the App

session = fo.launch_app(dataset)

  Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/dgural/Total-Segmentator-50.

Overview

This comprehensive Spine Segmentation Dataset is derived from the Total Segmentator dataset and meticulously categorized into distinct classes corresponding to various vertebrae segments. It is an invaluable resource for researchers and practitioners in the field of medical imaging, particularly those focused on spinal health.

Dataset Contents

The dataset features 1089 high-resolution CT scans, each accompanied by expertly segmented nifti files. The segmentation targets the spinal vertebrae, classifying them into the following specific types:

• Cervical Vertebrae: C1, C2, C3, C4, C5, C6
• Thoracic Vertebrae: T1, T2, T3, T4, T5, T6, T7, T8, T9, T10, T11, T12
• Lumbar Vertebrae: L1, L2, L3, L4, L5
• Sacral Vertebrae: S1

Dataset Utility

This dataset serves as an essential tool for developing and evaluating segmentation algorithms in medical imaging. It can be used for a variety of applications such as automated diagnostic systems, anatomical research, and advanced spine health monitoring techniques.

Usage:

Ideal for academic researchers, industry professionals, and anyone interested in the development of AI-based tools for spine imaging. This dataset is prepared to facilitate easy integration with existing machine learning frameworks and software.

In 298 MR images we segmented 56 anatomical structures covering a majority of relevant classes for most use cases. The MR images were randomly sampled from clinical routine, thus representing a real world dataset which generalizes to clinical application. The dataset contains a wide range of different pathologies, scanners, sequences and institutions. Moreover, it contains some images from IDC for further data diversity (see column "source" in meta.csv).

Link to a copy of this dataset on Dropbox for much quicker download: Dropbox Link

You can find a segmentation model trained on this dataset here.

More details about the dataset can be found in the corresponding paper. Please cite this paper if you use the dataset. The CT images described in the paper can be found here.

The following classes from the paper are not part of this dataset: subcutaneous_fat, torso_fat, skeletal_muscle, face_region

This dataset was created by the department of Research and Analysis at University Hospital Basel.

Brain CT Segmentation for medical diagnostics

The dataset contains over 1,000 studies encompassing 10 pathologies, providing a comprehensive resource for advancing research in brain imaging techniques. It comprises a wide variety of CT scans aimed at facilitating segmentation tasks related to brain tumors, lesions, and other brain structures. The data is provided in nii format and includes both volumetric data and the corresponding masks for each study, facilitating comprehensive… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/brain-ct-segmentation.

This dataset contains 2D image slices extracted from the publicly available Pancreas-CT-SEG dataset, which provides manually segmented pancreas annotations for contrast-enhanced 3D abdominal CT scans. The original dataset was curated by the National Institutes of Health Clinical Center (NIH) and was made available through the NCI Imaging Data Commons (IDC). The dataset consists of 82 CT scans from 53 male and 27 female subjects, converted into 2D slices for segmentation tasks.

Dataset Details:

Modality: Contrast-enhanced CT (portal-venous phase, ~70s post-injection)

Number of Subjects: 82

Age Range: 18 to 76 years (Mean: 46.8 ± 16.7 years)

Scan Resolution: 512 × 512 pixels per slice

Slice Thickness: Varies between 1.5 mm and 2.5 mm

Scanners Used: Philips and Siemens MDCT scanners (120 kVp tube voltage)

Segmentation: Manually performed by a medical student and verified by an expert radiologist

Data Format: Converted from 3D DICOM/NIfTI to 2D PNG/JPEG slices for segmentation tasks

Total Dataset Size: ~1.85 GB

Category: Non-cancerous healthy controls (No pancreatic cancer lesions or major abdominal pathologies)

Preprocessing and Conversion:

The original 3D CT scans and corresponding pancreas segmentation masks (available in NIfTI format) were converted into 2D slices to facilitate 2D medical image segmentation tasks. The conversion steps include:

Extracting axial slices from each 3D CT scan.

Normalizing pixel intensities for consistency.

Saving images in PNG/JPEG format for compatibility with deep learning frameworks.

Generating corresponding binary segmentation masks where the pancreas region is labeled.

Dataset Structure:

Applications

This dataset is ideal for medical image segmentation tasks such as:

Deep learning-based pancreas segmentation (e.g., using U-Net, DeepLabV3+)

Automated organ detection and localization

AI-assisted diagnosis and analysis of abdominal CT scans

Acknowledgments & References

This dataset is derived from:

National Cancer Institute Imaging Data Commons (IDC) [1]

The Cancer Imaging Archive (TCIA) [2]

Original dataset DOI: https://doi.org/10.7937/K9/TCIA.2016.tNB1kqBU

Citations: If you use this dataset, please cite the following:

Roth, H., Farag, A., Turkbey, E. B., Lu, L., Liu, J., & Summers, R. M. (2016). Data From Pancreas-CT (Version 2). The Cancer Imaging Archive. DOI: 10.7937/K9/TCIA.2016.tNB1kqBU

Fedorov, A., Longabaugh, W. J. R., Pot, D., Clunie, D. A., Pieper, S. D., Gibbs, D. L., et al. (2023). National Cancer Institute Imaging Data Commons: Toward Transparency, Reproducibility, and Scalability in Imaging Artificial Intelligence. Radiographics 43.

License: This dataset is provided under the Creative Commons Attribution 4.0 International (CC-BY-4.0) license. Users must abide by the TCIA Data Usage Policy and Restrictions.

Additional Resources: Imaging Data Commons (IDC) Portal: https://portal.imaging.datacommons.cancer.gov/explore/

OHIF DICOM Viewer: https://viewer.ohif.org/

This dataset provides a high-quality, well-annotated resource for researchers and developers working on medical image analysis, segmentation, and AI-based pancreas detection.

Total-Text is a dataset tailored for instance segmentation, semantic segmentation, and object detection tasks, containing 1555 images with 11165 labeled objects belonging to a single class — text with text label tag. Its primary aim is to open new research avenues in the scene text domain. Unlike traditional text datasets, Total-Text uniquely includes curved-oriented text in addition to horizontal and multi-oriented text, offering diverse text orientations in more than half of its images. This variety makes it a crucial resource for advancing text-related studies in computer vision and natural language processing.

Chest ST Segmentation of a pathology and anatomical regions

Dataset comprises 1,000+ studies, featuring 7 pathologies and covering 8 anatomical regions. It includes a variety of CT scans that facilitate research in lung segmentation and disease detection. Researchers can leverage this dataset for clinical practice, studying imaging data for better early detection methods and computer-aided screening. The data is provided in nii format and includes both volumetric data and the… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/chest-ct-segmentation.

The HaN-Seg: Head and Neck Organ-at-Risk CT & MR Segmentation Dataset is a publicly available dataset of anonymized head and neck (HaN) images of 42 patients that underwent both CT and T1-weighted MR imaging for the purpose of image-guided radiotherapy planning. In addition, the dataset also contains reference segmentations of 30 organs-at-risk (OARs) for CT images in the form of binary segmentation masks, which were obtained by curating manual pixel-wise expert image annotations. A full description of the HaN-Seg dataset can be found in:

G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "HaN-Seg: The head and neck organ-at-risk CT & MR segmentation dataset", Medical Physics, 2023. 10.1002/mp.16197,

and any research originating from its usage is required to cite this paper.

In parallel with the release of the dataset, the HaN-Seg: The Head and Neck Organ-at-Risk CT & MR Segmentation Challenge is launched to promote the development of new and application of existing state-of-the-art fully automated techniques for OAR segmentation in the HaN region from CT images that exploit the information of multiple imaging modalities, in this case from CT and MR images. The task of the HaN-Seg challenge is to automatically segment up to 30 OARs in the HaN region from CT images in the devised test set, consisting of 14 CT and MR images of the same patients, given the availability of the training set (i.e. the herein publicly available HaN-Seg dataset), consisting of 42 CT and MR images of the same patients with reference 3D OAR binary segmentation masks for CT images.

Please find below a list of relevant publications that address: (1) the assessment of inter-observer and inter-modality variability in OAR contouring, (2) results of the HaN-Seg challenge, (3) development of our multimodal segmentation model, and (4) development of MR-to-CT image-to-image translation using diffusion models:

1. G. Podobnik, B. Ibragimov, P. Strojan, P. Peterlin, T. Vrtovec, "vOARiability: Interobserver and intermodality variability analysis in OAR contouring from head and neck CT and MR images", Medical Physics, 2024. 10.1002/mp.16924,
2. G. Podobnik, B. Ibragimov, E. Tappeiner, et al., "HaN-Seg: The head and neck organ-at-risk CT and MR segmentation challenge", Radiotherapy and Oncology, 2024. 10.1016/j.radonc.2024.110410,
3. G. Podobnik, P. Strojan, P. Peterlin, B. Ibragimov, T. Vrtovec, "Multimodal CT and MR Segmentation of Head and Neck Organs-at-Risk", MICCAI 2023, 2023. 10.1007/978-3-031-43901-8_71,
4. R. M. Šter, G. Podobnik, T. Vrtovec, "Diffusion-based MR-to-CT translation of head and neck images", SPIE Medical Imaging 2025, 2025. 10.1117/12.3047458.

In total 50 cases segmented with liver segments 1-8.Free to use and download. Check out our 5 segment model at www.medseg.aiAll cases obtained Decathlon's dataset, see details and reference here: https://arxiv.org/abs/1902.09063Segmentations done by MedSeg#Update 2/4/21: 40 new cases added, case 1 replaced with new case

The Magnetic Resonance - Computed Tomography (MR-CT) Jordan University Hospital (JUH) dataset has been collected after receiving Institutional Review Board (IRB) approval of the hospital and consent forms have been obtained from all patients. All procedures has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki).

The dataset consists of 2D image slices extracted using the RadiAnt DICOM viewer software. The extracted images are transformed to DICOM image data format with a resolution of 256x256 pixels. There are a total of 179 2D axial image slices referring to 20 patient volumes (90 MR and 89 CT 2D axial image slices). The dataset contains MR and CT brain tumour images with corresponding segmentation masks. The MR images of each patient were acquired with a 5.00mm T Siemens Verio 3T using a T2-weighted without contrast agent, 3 Fat sat pulses (FS), 2500-4000 TR, 20-30 TE, and 90/180 flip angle. The CT images were acquired with Siemens Somatom scanner with 2.46mGY.cm dose length, 130KV voltage, 113-327 mAs tube current, topogram acquisition protocol, 64 dual source, one projection, and slice thickness of 7.0mm. Smooth and sharp filters have been applied to the CT images. The MR scans have a resolution of 0.7x0.6x5 mm^3, while the CT scans have a resolution of 0.6x0.6x7 mm^3.

More information and the application of the dataset can be found in the following research paper:

Alaa Abu-Srhan; Israa Almallahi; Mohammad Abushariah; Waleed Mahafza; Omar S. Al-Kadi. Paired-Unpaired Unsupervised Attention Guided GAN with Transfer Learning for Bidirectional Brain MR-CT Synthesis. Comput. Biol. Med. 136, 2021. doi: https://doi.org/10.1016/j.compbiomed.2021.104763.

Brain MR images and FLAIR abnormality segmentation masks created by hand are part of this dataset. These pictures came from TCIA, or The Cancer Imaging Archive. Their genetic cluster data and fluid-attenuated inversion recovery (FLAIR) sequences are from 110 patients with lower-grade glioma who are part of the Cancer Genome Atlas (TCGA) collection. You can find patient information and genomic clusters of tumours in the data.csv file. Deep Learning Projects for Final Year FYI: It is not my… See the full description on the dataset page: https://huggingface.co/datasets/gymprathap/Brain-MRI-LGG-Segmentation.

there are some dataset error, please see discussion: https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection/discussion/436096

Apply total segmentator[1] on rsna 2023 abdominal trauma dataset[2]. The command used is based on public notebook[3]

!TotalSegmentator \
-i /kaggle/input/rsna-2023-abdominal-trauma-detection/train_images/10104/27573 \
-o /kaggle/temp/masks \
-ot 'nifti' \
-rs spleen kidney_left kidney_right liver esophagus colon duodenum small_bowel stomach

NOTE: there are probably error (about 5%?) in the total segmentator results. Please do a check before using this dataset!!!

[1] https://github.com/wasserth/TotalSegmentator

[2] https://www.kaggle.com/competitions/rsna-2023-abdominal-trauma-detection

[3] https://www.kaggle.com/code/enriquezaf/totalsegmentator-offline

Spine MRI Dataset, Anomaly Detection & Segmentation

The dataset consists of .dcm files containing MRI scans of the spine of the person with several dystrophic changes, such as osteophytes, dorsal disc extrusions, dorsal disc protrusions and spondyloarthrosis. The images are labeled by the doctors and accompanied by report in PDF-format. The dataset includes 5 studies, made from the different angles which provide a comprehensive understanding of a several dystrophic changes and… See the full description on the dataset page: https://huggingface.co/datasets/TrainingDataPro/spine-segmentation-dataset.

ETIS

We established a large-scale plant disease segmentation dataset named PlantSeg. PlantSeg comprises more than 11,400 images of 115 different plant diseases from various environments, each annotated with its corresponding segmentation label for diseased parts. To the best of our knowledge, PlantSeg is the largest plant disease segmentation dataset containing in-the-wild images. Our dataset enables researchers to evaluate their models and provides a valid foundation for the development and benchmarking of plant disease segmentation algorithms.

Please note that due to the image limitations of Roboflow, the dataset provided here is not complete.

Project page: https://github.com/tqwei05/PlantSeg

Paper: https://arxiv.org/abs/2409.04038

Complete dataset download: https://zenodo.org/records/13958858

Reference: @article{wei2024plantseg, title={PlantSeg: A Large-Scale In-the-wild Dataset for Plant Disease Segmentation}, author={Wei, Tianqi and Chen, Zhi and Yu, Xin and Chapman, Scott and Melloy, Paul and Huang, Zi}, journal={arXiv preprint arXiv:2409.04038}, year={2024} }

Multiclass Semantic Segmentation Duckietown Dataset

A dataset of multiclass semantic segmentation image annotations for the first 250 images of the "Duckietown Object Detection Dataset".

Raw Image Segmentated Image

  Semantic Classes

This dataset defines 8 semantic classes (7 distinct classes + implicit background class):

Class XML Label Description Color (RGB)

Ego Lane Ego Lane The lane the agent is supposed to be driving in (default right-hand… See the full description on the dataset page: https://huggingface.co/datasets/hamnaanaa/Duckietown-Multiclass-Semantic-Segmentation-Dataset.

we introduce Med-DDPM

Fast and accurate segmentation of knee bone and cartilage on MRI images is becoming increasingly important in the orthopaedic area, as the segmentation is an essential prerequisite step to a patient-specific diagnosis, optimising implant design and preoperative and intraoperative planning. However, manual segmentation is time-intensive and subjected to inter- and intra-observer variations. Hence, in this study, a three-dimensional (3D) deep neural network using adversarial loss was proposed to automatically segment the knee bone in a resampled image volume in order to enlarge the contextual information and incorporate prior shape constraints. A restoration network was proposed to further improve the bone segmentation accuracy by restoring the bone segmentation back to the original resolution. A conventional U-Net-like network was used to segment the cartilage. The ultimate results were the combination of the bone and cartilage outcomes through post-processing. The quality of the proposed method was thoroughly assessed using various measures for the dataset from the Grand Challenge Segmentation of Knee Images 2010 (SKI10), together with a comparison with a baseline network U-Net. A fine-tuned U-Net-like network can achieve state-of-the-art results without any post-processing operations. This method achieved a total score higher than 76 in terms of the SKI10 validation dataset. This method showed to be robust to extract bone and cartilage masks from the MRI dataset, even for the pathological case.

Retinal OCT-Angiography Vessel Segmentation Dataset (ROSE) is now open source. It includes two subsets: ROSE-1 and ROSE-2.

• ROSE-1 subset consists of a total of 117 OCTA images from 39 subjects (26 with disease and the rest are healthy control) and was split into 90 images for training and 27 images for testing. Each subject has en face angiograms of superficial (SVC), deep (DVC), and the inner retinal vascular plexus that includes both SVC and DVC (SVC+DVC) respectively. All the OCTA scans were captured by the RTVue XR Avanti SD-OCT system (Optovue, USA) equipped with AngioVue software, with image resolution of 304 × 304 pixels. The scan area was 3×3 mm² area centred at the fovea. The manual annotations of these vascular network were graded by image experts and clinicians, and the consensus of them was then used as the groundtruth. Two different types of vessel annotations are available: centerline-level and pixel-level annotation.
• ROSE-2 subset contains a total of 112 OCT-A images of 112 eyes acquired by Heidelberg OCT2 system with Spectralis software (Heidelberg Engineering, Heidelberg, Germany) and was split into 90 images for training and 22 images for testing. All the images in this dataset are the en face angiograms of SVC within a 3×3 mm² area centred at the fovea, and each image was resized into a grayscale image with 840 × 840 pixels. All the visible retinal vessels were manually traced using an in-house program written in Matlab (Mathworks R2018, Natwick) by an experienced clinical ophthalmologist.

code: iMED-Lab/OCTA-Net-OCTA-Vessel-Segmentation-Network (github.com)

ROSE-O is a retinal structure detection dataset of OCTA images, with precise manual annotations of RV, RVJ and the FAZ. It contains 117 images which were captured using the Optovue Avanti RTVue XR with AngioVue software (Optovue, Fremont, USA): the images have a resolution of 304×304 pixels. The SVC, DVC and IVC angiograms of each participant were obtained by the device.

code: https://github.com/iMED-Lab/VAFF-Net