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

About

This is a derivative of the TotalSegmentator dataset

616 MR images and corresponding segmentation mask of 50 structures We combined multiple segmentation masks into a single nii.gz file under the folder Masks, and moved all MR images to the folder Images. All images and masks are renamed according to case IDs.

This dataset is released under the CC BY-NC-SA 2.0 license.

  News 🔥

[10 Oct, 2025] This dataset is integrated into 🔥MedVision🔥… See the full description on the dataset page: https://huggingface.co/datasets/YongchengYAO/TotalSegmentator-MR-Lite.

Info: This is version 2 of the TotalSegmentator dataset.

In 1228 CT images we segmented 117 anatomical structures 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.

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

Overview of differences to v1 of this dataset: here

A small subset of this dataset with only 102 subjects for quick download+exploration can be found here: here

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

More details about the dataset can be found in the corresponding paper (the paper describes v1 of the dataset). Please cite this paper if you use the dataset.

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

This dataset contributes volumetric segmentations of the anatomic regions in a subset of CT images available from NCI Imaging Data Commons [1] (https://imaging.datacommons.cancer.gov/) automatically generated using the TotalSegmentation model v1.5.6 [2]. The initial release includes segmentations for the majority of the CT scans included in the National Lung Screening Trial (NLST) collection [3], [4] already available in IDC. Direct link to open this analysis result dataset in IDC (available after release of IDC v18): https://portal.imaging.datacommons.cancer.gov/explore/filters/?analysis_results_id=TotalSegmentator-CT-Segmentations.

Specifically, for each of the CT series analyzed, we include segmentations as generated by TotalSegmentator, converted into DICOM Segmentation object format using dcmqi v1.3.0 [5], and first order and shape features for each of the segmented regions, as produced by pyradiomics v3.0.1 [6]. Radiomics features were converted to DICOM Structured Reporting documents following template TID1500 using dcmqi. TotalSegmentator analysis on the NLST cohort was executed using Terra platform [7]. Implementation of the workflow that was used for performing the analysis is available at https://github.com/ImagingDataCommons/CloudSegmentator [8].

Due to the large size of the files, they are stored in the cloud buckets maintained by IDC, and the attached files are the manifests that can be used to download the actual files.

If you use the files referenced in the attached manifests, we ask you to cite this dataset and the preprint describing how it was generated [9].

Download instructions

Each of the manifests include instructions in the header on how to download the included files.

To download the TotalSegmentator segmentations (in DICOM SEG format) and pyradiomics measurements (in DICOM SR format) files using .s5cmd manifests:

1. install idc-index package: pip install --upgrade idc-index
2. download the files referenced by manifests included in this dataset by passing the .s5cmd manifest file. E.g., idc download totalsegmentator_ct_segmentations_aws.s5cmd

Other files included in the record are:

1. firstorder and shape radiomics features extracted using pyradiomics, and organized one file per segmented structure (see README file in the zip file for details on how those are organized)
   1. pyradiomics_features_csv.zip: saved in CSV format
   2. pyradiomics_features_parquet.zip: saved in Parquet format

Support

If you have any questions about this dataset, or if you experience any issues, please reach out to Imaging Data Commons support via support@canceridc.dev or (preferred) IDC Forum at https://discourse.canceridc.dev.

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.

Dataset

This dataset was created by Elahi

Contents

Robust segmentation of 104 anatomical structures in CT images.

TotalSegmentator Ribs Dataset

  Dataset Description

The TotalSegmentator Ribs dataset for rib segmentation (TotalSegmentator Ribs subset). This dataset contains CT scans with dense segmentation annotations.

  Dataset Details

Modality: CT Target: individual ribs Format: NIfTI (.nii.gz)

  Dataset Structure

Each sample in the JSONL file contains: { "image": "path/to/image.nii.gz", "mask": "path/to/mask.nii.gz", "label": ["organ1", "organ2", ...]… See the full description on the dataset page: https://huggingface.co/datasets/Angelou0516/totalsegmentator-ribs.

TotalSegmentator Organs Dataset

  Dataset Description

The TotalSegmentator Organs dataset for multi-organ segmentation (TotalSegmentator Organs subset). This dataset contains CT scans with dense segmentation annotations.

  Dataset Details

Modality: CT Target: adrenal glands, colon, duodenum, esophagus, gallbladder, kidneys, liver, lungs, pancreas, small bowel, spleen, stomach, trachea, bladder Format: NIfTI (.nii.gz)

  Dataset Structure

Each sample in… See the full description on the dataset page: https://huggingface.co/datasets/Angelou0516/totalsegmentator-organs.

Dataset

This dataset was created by coderRKJ

Released under CC0: Public Domain

Contents

TotalSegmentator Cardiac Dataset

  Dataset Description

The TotalSegmentator Cardiac dataset for cardiac structures segmentation (TotalSegmentator Cardiac subset). This dataset contains CT scans with dense segmentation annotations.

  Dataset Details

Modality: CT Target: heart, atria, ventricles, aorta, pulmonary artery Format: NIfTI (.nii.gz)

  Dataset Structure

Each sample in the JSONL file contains: { "image": "path/to/image.nii.gz", "mask":… See the full description on the dataset page: https://huggingface.co/datasets/Angelou0516/totalsegmentator-cardiac.

TotalSegmentator Vertebrae Dataset

  Dataset Description

The TotalSegmentator Vertebrae dataset for vertebrae segmentation (TotalSegmentator Vertebrae subset). This dataset contains CT scans with dense segmentation annotations.

  Dataset Details

Modality: CT Target: cervical, thoracic, and lumbar vertebrae Format: NIfTI (.nii.gz)

  Dataset Structure

Each sample in the JSONL file contains: { "image": "path/to/image.nii.gz", "mask":… See the full description on the dataset page: https://huggingface.co/datasets/Angelou0516/totalsegmentator-vertebrae.

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.

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.

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

Dataset

This dataset was created by JavaZero

Contents

Dataset

This dataset was created by Avni Mittal

Released under MIT

Contents

stranger47/totalsegmentator-mesh-dataset-250929-clean0.5 dataset hosted on Hugging Face and contributed by the HF Datasets community

stranger47/totalsegmentator-mesh-dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

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

If you plan to use this dataset, please cite the following paper:

Truong, T., Baltruschat, I.M., Klemens, M. et al. Exploring AI-Based System Design for Pixel-Level Protected Health Information Detection in Medical Images. J Digit Imaging. Inform. med. (2025). https://doi.org/10.1007/s10278-025-01619-y

This dataset includes two collections: RadPHI-test and MIDI.

RadPHI-test includes 1000 images across four modalities: CT, chest X-ray, radionuclide bone scan, and MRI images overlaid with synthetic texts. Images are sourced from the following datasets: TotalSegmentator [1] for CT, BS-80K [2] for bone scans, ChestX-ray8 [3] for chest X-rays, and BRATS[4] for brain MRI. The imprints are synthetically generated over 16 categories, six of which are considered PHI: patient name, address, identifier, phone number, email, and date. Of the 1000 images, 850 contain at least one type of PHI imprint.

MIDI is curated from the validation and test set of the 2024 Medical Image De-Identification Benchmark (MIDI-B) challenge [5], which is available on The Cancer Imaging Archive [6]. This dataset originally consists of 605 studies across multiple modalities, each containing synthetic PHI content embedded at both the DICOM header and pixel level. We utilize a DICOM viewer, specifically MD.ai [7], to overlay the DICOM tags onto the images. We randomly sample DICOM tags to ensure that the generated imprints represent all possible PHI categories, similar to the RadPHI-test dataset. After applying the overlays, we export the images from the viewer. The resulting images may include not only the DICOM tag overlays but also burn-ins by the challenge organizers. The final version of the dataset comprises 550 images categorized into five PHI types: patient name, address, identifier, phone number, and date. We performed instance-level annotation of the images by generating coordinates for PHI instances along with their corresponding categories. This annotation process was carried out and validated by two independent annotators to ensure accuracy and reliability.

[1] Wasserthal J, Breit HC, Meyer MT, Pradella M, Hinck D, Sauter AW, Heye T, Boll DT, Cyriac J, Yang S, et al.: TotalSegmentator: robust segmentation of 104 anatomic structures in CT images. Radiol Artif Intell 5(5), 2023.

[2]Huang Z, Pu X, Tang G, Ping M, Jiang G, Wang M, Wei X, Ren Y: BS-80K: The first large open-access dataset of bone scan images. Comput Biol Med 151:106221, 2022.

[3] Wang X, Peng Y, Lu L, Lu Z, Bagheri M, Summers RM: ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2097–2106, 2017.

[4]Antonelli M, Reinke A, Bakas S, Farahani K, Kopp-Schneider A, Landman BA, Litjens G, Menze B, Ronneberger O, Summers RM, et al.: The medical segmentation decathlon. Nat Commun 13(1):4128, 2022.

[5] Farahani K, Clunie D, Klenk J, Kopchick B, Diaz M, Pan Q, Pei L, Prior F, Rutherford M, Singh A, Sutton G, Wagner U: Medical Image De-Identification Benchmark (MIDI-B). Available at https://www.synapse.org/Synapse:syn53065760 Accessed 16 April 2025.

[6] Rutherford MW, Nolan T, Pei L, Wagner U, Pan Q, Farmer P, Smith K, Kopchick B, Opsahl-Ong L, Sutton G, Clunie DA, Farahani K, Prior F: Data in support of the MIDI-B Challenge (MIDI-B-Synthetic-Validation, MIDI-B-Curated-Validation, MIDI-B-Synthetic-Test, MIDI-B-Curated-Test) (Version 1) [Data set]. The Cancer Imaging Archive, https://doi.org/10.7937/cf2p-aw56, 2025

[7] MD.ai. Available at https://www.md.ai. Accessed 28 April 2025.

Whole-Spine Anatomical MRI dataset & B0 simulations

Dataset Description

This dataset includes structural MRI (T1-weighted) and simulated ΔB0 field maps for sixty volunteers. Participants were scanned using two Siemens 3T MRI scanners (MAGNETOM Tim Trio and Verio) equipped with head, neck, and spine coils. The scans cover anatomical regions extending from the head to the torso and include lateral torso encompassing most of both lungs.

All data is organized in BIDS format and is available on OpenNeuro.

Participants

• Total Participants: 60
• Males: 32
• Females: 18
• Undisclosed sex: 10
• Age: Mean = 27.1 years, SD = 6.5, Range = 21-56 years
• Weight: Mean = 66.7 kg, SD = 9.5, Range = 45-90 kg
• Height: Mean = 175.6 cm, SD = 8.8, Range = 155-192 cm

MRI Acquisition

• Scanner Models: Siemens MAGNETOM Tim Trio and Verio (3T)
• Coils Used: Head, neck, and spine coils
• Structural Images: T1-weighted MPRAGE
• Resolution: 1 mm³
• Field of View (FOV): From head to torso, including lateral regions of both lungs
• Data Processing

Structural Data Segmentation

1. Automated Segmentation Tools:

   • TotalSegmentator MRI: Used for full-body, sinuses, trachea, ear canal, and lungs based on training with 10 manually segmented subjects.
   • Samseg: Used for segmenting brain, eyes, and skull.
   • TotalSpineSeg: Used for segmenting spinal cord, vertebrae, and intervertebral disks.

2. Post-Processing Steps:

   • Tissue islands were removed, holes were closed, and tissue masks for specific regions (skull, brain, eyes, sinus, and ear canal) were smoothed using a custom pipeline (GitHub repo, release v1.1, commit: 4f3c471db542fa9b12f308aaeece401323980965).
   • Tissue masks were then merged into a single NIfTI file with the following voxel assignments: background (air), body, brain, spine, lungs, skull, trachea, sinus, ear canal, and eyes.

Susceptibility Assignment

Each anatomical label in the segmentation volumes was assigned a specific susceptibility value (χ) as defined in this Github repository:

• Air: 0.35 ppm
• Sinus & Ear Canals: -2 ppm
• Trachea & Lungs: -4.2 ppm
• Brain: -9.04 ppm
• Body & Eyes: -9.05 ppm
• Spinal Canal & Disks: -9.055 ppm
• Skull & Vertebrae: -11 ppm

Field Map Simulation

Field maps (ΔB0) were generated by applying a convolution in the Fourier domain between the susceptibility maps and an analytical dipole distribution. Key parameters:

• Implementation: Python (GitHub repo)
• Padding:
  • Edge-value padding applied on five volume surfaces
  • Constant-value padding applied on the dorsal surface
  • Padding Size: 50 voxels per surface

Dataset Files and Structure

This dataset is organized according to the BIDS format. Key directories and files include:

• /sub-
• /derivatives: Includes simulated ΔB0 field maps and segmentation labels