Update README.md

README.md

@@ -1,33 +1,53 @@
 ---
 license: apache-2.0
 tags:
-- medical
+- multi-modal
 - 3D medical segmentation
 size_categories:
 - 1K<n<10K
 ---
 
+![Data_visualization](M3D_Seg.jpg)
+
 ## 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.
-
+3D medical image segmentation poses a significant challenge in medical image analysis. 
+Currently, due to privacy and cost constraints, publicly available large-scale 3D medical images 
+and their annotated data are scarce. 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 to 0024, totaling 5,772 3D images and 149,196 3D mask annotations.
+The semantic labels corresponding to each mask can be represented in text. 
+Within each sub-dataset folder, there are multiple data folders (containing image and mask files), 
+and each sub-dataset independently utilizes its JSON file to split.
+
+- **data_load_demo.py**: Provides an example code on reading images and masks from the dataset.
+- **data_preocess.py**: Describes how to convert raw `nii.gz` or other format data into a more efficient `npy` format and preprocess them,
+saving them in a unified format. This dataset has already been preprocessed, so there is no need to use data_preocess.py again.
+If adding new datasets, please follow a unified processing approach.
+- **dataset_info.json & dataset_info.txt**: Contain the names of each dataset and their label texts.
+- **term_dictionary.json**: Provides multiple definitions or descriptions for each semantic label in the dataset,
+generated by `ChatGPT` for each term. Researchers can convert category IDs in the dataset to label texts
+using the information in dataset_info.txt and further convert them into text descriptions using term_dictionary.json as text inputs for segmentation models,
+enabling tasks such as segmentation based on text prompts and referring segmentation.
+
+This dataset supports not only traditional semantic segmentation tasks but also text-based segmentation tasks. 
+For detailed methods, please refer to [SegVol](https://github.com/BAAI-DCAI/SegVol) and [M3D](https://github.com/BAAI-DCAI/M3D). 
+As a general segmentation dataset, we provide a convenient, unified, and structured dataset organization 
+that allows for the uniform integration of more public and private datasets in the same format as this dataset,
+thereby constructing a larger-scale general 3D medical image 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.
+This dataset not only supports traditional image-mask semantic segmentation tasks
+but also represents data in the form of image-mask-text, where masks can be converted into box coordinates 
+through bounding boxes. Based on this, the dataset can effectively support a series of image segmentation 
+and positioning tasks, as follows:
+
+- **3D Segmentation**: Semantic segmentation, text-based segmentation, referring segmentation, reasoning segmentation, etc.
+- **3D Positioning**: Visual grounding/referring expression comprehension, referring expression generation.
 
 ## Dataset Format and Structure
 
@@ -35,48 +55,71 @@ the dataset supports tasks such as: 3D segmentation: semantic segmentation, text
 <pre>
     M3D_Seg/
         0000/
-            ct/
-                case_00000.npy
-                ......
-            gt/
-                case_00000.(3, 512, 512, 611).npz
-                ......
+            1/
+                image.npy
+                mask_(1, 512, 512, 96).npz
+            2/
+            ......
             0000.json
         0001/
         ......
 </pre>
 
 ### Dataset Download
+The total dataset size is approximately 224G.
+
 #### Clone with HTTP
 ```bash
-git clone 
+git clone https://huggingface.co/datasets/GoodBaiBai88/M3D-Seg
+```
+
+#### SDK Download
+```bash
+from datasets import load_dataset
+dataset = load_dataset("GoodBaiBai88/M3D-Seg")
 ```
 #### 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.
+Manually download all files from the dataset files. It is recommended to use batch download tools for efficient downloading.
+Please note the following:
+
+- **Downloading in Parts and Merging**: Since dataset 0024 has a large volume,
+the original compressed file has been split into two parts: `0024_1` and `0024_2`.
+Make sure to download these two files separately and unzip them in the same directory to ensure data integrity.
+
+- **Masks with Sparse Matrices**: To save storage space effectively,
+foreground information in masks is stored in sparse matrix format and saved with the extension `.npz`.
+The name of each mask file typically includes its shape information for identification and loading purposes.
+
+- **Data Load Demo**: There is a script named data_load_demo.py, which serves as a reference for correctly
+reading the sparse matrix format of masks and other related data.
+Please refer to this script for specific loading procedures and required dependencies.
+
 
 
 ### 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.
+#### 1. Direct Usage of Preprocessed Data
+If you have already downloaded the preprocessed dataset, no additional data processing steps are required. 
+You can directly jump to step 2 to build and load the dataset. 
+Please note that the contents provided by this dataset have been transformed and numbered through data_process.py,
+differing from the original `nii.gz` files. To understand the specific preprocessing process, 
+refer to the data_process.py file for detailed information. If adding new datasets or modifying existing ones, 
+please refer to data_process.py for data preprocessing and uniform formatting.
+
 
 #### 2. Build Dataset 
-We provide sample code for three tasks' Datasets, including semantic segmentation, hint segmentation, and inference segmentation.
+To facilitate model training and evaluation using this dataset, we provide an example code for the Dataset class. 
+Wrap the dataset in your project according to the following example:
 
 ```python
 
 ```
 
 
+### Data Splitting
+Each sub-dataset folder is splitted into `train` and `test` parts through a JSON file, 
+facilitating model training and testing.
 
 
-### Data Splitting
-Each file is split into ‘train, validation/test’ using json files, for ease of training and testing models.
 
 ### Dataset Sources
 
@@ -89,7 +132,7 @@ Each file is split into ‘train, validation/test’ using json files, for ease
 | 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|
+| 0007 |BTCV|	https://www.synapse.org/#!Synapse:syn3193805/wiki/217753|
 | 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/|
@@ -111,7 +154,7 @@ Each file is split into ‘train, validation/test’ using json files, for ease
 
 ## Dataset Copyright Information
 
-All datasets involved in this dataset are publicly available datasets. For detailed copyright information, please refer to the corresponding dataset links.
+All datasets in this dataset are publicly available. For detailed copyright information, please refer to the corresponding dataset links.
 
 ## Citation
 If you use this dataset, please cite the following works: