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license: apache-2.0
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---
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---
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license: apache-2.0
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tags:
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- medical
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- 3D medical segmentation
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size_categories:
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- 1K<n<10K
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---
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## Dataset Description
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Large-scale General 3D Medical Image Segmentation Dataset (M3D-Seg)
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### Dataset Introduction
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3D medical segmentation is one of the main challenges in medical image analysis.
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Currently, due to privacy and cost limitations, there is a lack of large-scale publicly available 3D medical images and annotations.
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To address this, we have collected 25 publicly available 3D CT segmentation datasets,
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including CHAOS, HaN-Seg, AMOS22, AbdomenCT-1k, KiTS23, KiPA22, KiTS19, BTCV, Pancreas-CT, 3D-IRCADB, FLARE22, TotalSegmentator,
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CT-ORG, WORD, VerSe19, VerSe20, SLIVER07, QUBIQ, MSD-Colon, MSD-HepaticVessel, MSD-Liver, MSD-lung, MSD-pancreas, MSD-spleen,
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LUNA16. These datasets are uniformly encoded from 0000-0024, totaling 5,772 3D images and 149,196 3D mask annotations.
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Each mask corresponds to semantic labels represented in text.
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Within each folder, there are two sub-folders, ct and gt, storing data and annotations respectively, and utilizing json files for splitting.
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‘dataset_info.txt’ describes the textual representation of each dataset label.
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As a universal segmentation dataset, more public and private datasets can be unified in the same format,
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thus building a large-scale 3D medical universal segmentation dataset.
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### Supported Tasks
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As data can be represented in the form of image-mask-text, where masks can be converted to box coordinates through bounding boxes,
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the dataset supports tasks such as: 3D segmentation: semantic segmentation, textual hint segmentation, inference segmentation, etc.
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3D localization: visual grounding, referring expression comprehension, referring expression generation.
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## Dataset Format and Structure
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### Data Format
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<pre>
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M3D_Seg/
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0000/
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ct/
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case_00000.npy
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......
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gt/
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case_00000.(3, 512, 512, 611).npz
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......
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0000.json
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0001/
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......
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</pre>
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### Dataset Download
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#### Clone with HTTP
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```bash
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git clone
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```
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#### Manual Download
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Download all files from the dataset file manually, which can be done using batch download tools.
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Note: Since the 0024 dataset is large, its compressed files are split into 00, 01, 02 three files.
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Please merge and decompress them after downloading.
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As the foreground in mask files is often sparse, to save storage space, we use sparse matrices for storage, saved as npz files,
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with the file name containing the mask shape, please refer to ‘data_load_demo.py’ for data reading.
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### Dataset Loading Method
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#### 1. If downloading this dataset directly, ‘data_process.py’ is not required for processing, skip directly to step 2
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Raw data downloaded from the original data must be processed through ‘data_process.py’ and unified into the M3D-Seg dataset.
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Please note that due to preprocessing, there are differences between the data provided by this dataset and its original nii.gz files.
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Please refer to ‘data_process.py’ for processing methods.
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#### 2. Build Dataset
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We provide sample code for three tasks' Datasets, including semantic segmentation, hint segmentation, and inference segmentation.
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```python
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```
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### Data Splitting
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Each file is split into ‘train, validation/test’ using json files, for ease of training and testing models.
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### Dataset Sources
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| ID | Dataset | Link |
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| ------------- | ------------- | ------------- |
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| 0000 |CHAOS| https://chaos.grand-challenge.org/ |
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| 0001 |HaN-Seg| https://han-seg2023.grand-challenge.org/|
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| 0002 |AMOS22| https://amos22.grand-challenge.org/|
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| 0003 |AbdomenCT-1k| https://github.com/JunMa11/AbdomenCT-1K|
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| 0004 |KiTS23| https://kits-challenge.org/kits23/|
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| 0005 |KiPA22| https://kipa22.grand-challenge.org/|
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| 0006 |KiTS19| https://kits19.grand-challenge.org/|
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| 0007 |BTCV| https://www.synapse.org/\#!Synapse:syn3193805/wiki/217752|
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| 0008 |Pancreas-CT| https://wiki.cancerimagingarchive.net/display/public/pancreas-ct|
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| 0009 | 3D-IRCADB | https://www.kaggle.com/datasets/nguyenhoainam27/3dircadb |
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| 0010 |FLARE22| https://flare22.grand-challenge.org/|
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| 0011 |TotalSegmentator| https://github.com/wasserth/TotalSegmentator|
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| 0012 |CT-ORG| https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=61080890|
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| 0013 |WORD| https://paperswithcode.com/dataset/word|
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| 0014 |VerSe19| https://osf.io/nqjyw/|
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| 0015 |VerSe20| https://osf.io/t98fz/|
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| 0016 |SLIVER07| https://sliver07.grand-challenge.org/|
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| 0017 |QUBIQ| https://qubiq.grand-challenge.org/|
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| 0018 |MSD-Colon| http://medicaldecathlon.com/|
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| 0019 |MSD-HepaticVessel| http://medicaldecathlon.com/|
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| 0020 |MSD-Liver| http://medicaldecathlon.com/|
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| 0021 |MSD-lung| http://medicaldecathlon.com/|
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| 0022 |MSD-pancreas| http://medicaldecathlon.com/|
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| 0023 |MSD-spleen| http://medicaldecathlon.com/|
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| 0024 |LUNA16| https://luna16.grand-challenge.org/Data/|
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## Dataset Copyright Information
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All datasets involved in this dataset are publicly available datasets. For detailed copyright information, please refer to the corresponding dataset links.
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## Citation
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If you use this dataset, please cite the following works:
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```BibTeX
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@misc{bai2024m3d,
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title={M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models},
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author={Fan Bai and Yuxin Du and Tiejun Huang and Max Q. -H. Meng and Bo Zhao},
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year={2024},
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eprint={2404.00578},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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@misc{du2024segvol,
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title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation},
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author={Yuxin Du and Fan Bai and Tiejun Huang and Bo Zhao},
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year={2024},
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eprint={2311.13385},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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