Update README.md

README.md

@@ -1,3 +1,136 @@
 ---
 license: apache-2.0
+tags:
+- medical
+- 3D medical segmentation
+size_categories:
+- 1K<n<10K
 ---
+
+## Dataset Description
+Large-scale General 3D Medical Image Segmentation Dataset (M3D-Seg)
+
+### Dataset Introduction
+3D medical segmentation is one of the main challenges in medical image analysis. 
+Currently, due to privacy and cost limitations, there is a lack of large-scale publicly available 3D medical images and annotations. 
+To address this, we have collected 25 publicly available 3D CT segmentation datasets, 
+including CHAOS, HaN-Seg, AMOS22, AbdomenCT-1k, KiTS23, KiPA22, KiTS19, BTCV, Pancreas-CT, 3D-IRCADB, FLARE22, TotalSegmentator,
+CT-ORG, WORD, VerSe19, VerSe20, SLIVER07, QUBIQ, MSD-Colon, MSD-HepaticVessel, MSD-Liver, MSD-lung, MSD-pancreas, MSD-spleen,
+LUNA16. These datasets are uniformly encoded from 0000-0024, totaling 5,772 3D images and 149,196 3D mask annotations. 
+Each mask corresponds to semantic labels represented in text. 
+Within each folder, there are two sub-folders, ct and gt, storing data and annotations respectively, and utilizing json files for splitting. 
+‘dataset_info.txt’ describes the textual representation of each dataset label. 
+As a universal segmentation dataset, more public and private datasets can be unified in the same format, 
+thus building a large-scale 3D medical universal segmentation dataset.
+
+
+### Supported Tasks
+As data can be represented in the form of image-mask-text, where masks can be converted to box coordinates through bounding boxes,
+the dataset supports tasks such as: 3D segmentation: semantic segmentation, textual hint segmentation, inference segmentation, etc. 
+3D localization: visual grounding, referring expression comprehension, referring expression generation.
+
+## Dataset Format and Structure
+
+### Data Format
+<pre>
+    M3D_Seg/
+        0000/
+            ct/
+                case_00000.npy
+                ......
+            gt/
+                case_00000.(3, 512, 512, 611).npz
+                ......
+            0000.json
+        0001/
+        ......
+</pre>
+
+### Dataset Download
+#### Clone with HTTP
+```bash
+git clone 
+```
+#### Manual Download
+Download all files from the dataset file manually, which can be done using batch download tools. 
+Note: Since the 0024 dataset is large, its compressed files are split into 00, 01, 02 three files. 
+Please merge and decompress them after downloading. 
+As the foreground in mask files is often sparse, to save storage space, we use sparse matrices for storage, saved as npz files, 
+with the file name containing the mask shape, please refer to ‘data_load_demo.py’ for data reading.
+
+
+### Dataset Loading Method
+#### 1. If downloading this dataset directly, ‘data_process.py’ is not required for processing, skip directly to step 2
+Raw data downloaded from the original data must be processed through ‘data_process.py’ and unified into the M3D-Seg dataset. 
+Please note that due to preprocessing, there are differences between the data provided by this dataset and its original nii.gz files. 
+Please refer to ‘data_process.py’ for processing methods.
+
+#### 2. Build Dataset 
+We provide sample code for three tasks' Datasets, including semantic segmentation, hint segmentation, and inference segmentation.
+
+```python
+
+```
+
+
+
+
+### Data Splitting
+Each file is split into ‘train, validation/test’ using json files, for ease of training and testing models.
+
+### Dataset Sources
+
+| ID | Dataset  | Link |
+| ------------- | ------------- | ------------- |
+| 0000 |CHAOS|	https://chaos.grand-challenge.org/ |
+| 0001 |HaN-Seg|	https://han-seg2023.grand-challenge.org/|
+| 0002 |AMOS22|	https://amos22.grand-challenge.org/|
+| 0003 |AbdomenCT-1k|	https://github.com/JunMa11/AbdomenCT-1K|
+| 0004 |KiTS23|	https://kits-challenge.org/kits23/|
+| 0005 |KiPA22|	https://kipa22.grand-challenge.org/|
+| 0006 |KiTS19|	https://kits19.grand-challenge.org/|
+| 0007 |BTCV|	https://www.synapse.org/\#!Synapse:syn3193805/wiki/217752|
+| 0008 |Pancreas-CT|	https://wiki.cancerimagingarchive.net/display/public/pancreas-ct|
+| 0009 | 3D-IRCADB  | https://www.kaggle.com/datasets/nguyenhoainam27/3dircadb |
+| 0010 |FLARE22|	https://flare22.grand-challenge.org/|
+| 0011 |TotalSegmentator|	https://github.com/wasserth/TotalSegmentator|
+| 0012 |CT-ORG|	https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=61080890|
+| 0013 |WORD|	https://paperswithcode.com/dataset/word|
+| 0014 |VerSe19|	https://osf.io/nqjyw/|
+| 0015 |VerSe20|	https://osf.io/t98fz/|
+| 0016 |SLIVER07|	https://sliver07.grand-challenge.org/|
+| 0017 |QUBIQ|	https://qubiq.grand-challenge.org/|
+| 0018 |MSD-Colon|	http://medicaldecathlon.com/|
+| 0019 |MSD-HepaticVessel|	http://medicaldecathlon.com/|
+| 0020 |MSD-Liver|	http://medicaldecathlon.com/|
+| 0021 |MSD-lung|  	http://medicaldecathlon.com/|
+| 0022 |MSD-pancreas|	http://medicaldecathlon.com/|
+| 0023 |MSD-spleen|	http://medicaldecathlon.com/|
+| 0024 |LUNA16|	https://luna16.grand-challenge.org/Data/|
+
+
+## Dataset Copyright Information
+
+All datasets involved in this dataset are publicly available datasets. For detailed copyright information, please refer to the corresponding dataset links.
+
+## Citation
+If you use this dataset, please cite the following works:
+
+```BibTeX
+@misc{bai2024m3d,
+      title={M3D: Advancing 3D Medical Image Analysis with Multi-Modal Large Language Models}, 
+      author={Fan Bai and Yuxin Du and Tiejun Huang and Max Q. -H. Meng and Bo Zhao},
+      year={2024},
+      eprint={2404.00578},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+@misc{du2024segvol,
+      title={SegVol: Universal and Interactive Volumetric Medical Image Segmentation}, 
+      author={Yuxin Du and Fan Bai and Tiejun Huang and Bo Zhao},
+      year={2024},
+      eprint={2311.13385},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```