Update README.md

- Added instructions for using 2D pose data
- Added guidelines for converting 2D pose data into heatmaps

datasets/Scoliosis1K/README.md

@@ -1,34 +1,85 @@
 # Tutorial for [Scoliosis1K](https://zhouzi180.github.io/Scoliosis1K)
+## Download the Scoliosis1K Dataset
 
-## Download the Scoliosis1K dataset
+You can download the dataset from the [official website](https://zhouzi180.github.io/Scoliosis1K).
-Download the dataset from the [link](https://zhouzi180.github.io/Scoliosis1K).
+The dataset is provided as four compressed files:
-decompress these two file by following command:
-```shell
-unzip -P password Scoliosis1K-pkl.zip   | xargs -n1 tar xzvf
-```
-password should be obtained by signing [agreement](https://zhouzi180.github.io/Scoliosis1K/static/resources/Scoliosis1KAgreement.pdf) and sending to email (12331257@mail.sustech.edu.cn)
 
-Then you will get Scoliosis1K formatted as:
+* `Scoliosis1K-sil-raw.zip`
-```
+* `Scoliosis1K-sil-pkl.zip`
-    DATASET_ROOT/
+* `Scoliosis1K-pose-raw.zip`
-        00000 (subject)/
+* `Scoliosis1K-pose-pkl.zip`
-            positive (category)/
+
-                    000-180 (view)/
+We recommend using the provided pickle (`.pkl`) files for convenience.
-                        000.pkl (contains all frames)
+Decompress them with the following commands:
-        ......
+
-```
+```bash
-## Train the dataset
+unzip -P <password> Scoliosis1K-sil-pkl.zip
-Modify the `dataset_root` in `configs/sconet/sconet_scoliosis1k.yaml`, and then run this command:
+unzip -P <password> Scoliosis1K-pose-pkl.zip
-```shell
-CUDA_VISIBLE_DEVICES=0,1,2,3 python -m torch.distributed.launch --nproc_per_node=4 opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase train
 ```
 
+> **Note**: The \<password\> can be obtained by signing the [release agreement](https://zhouzi180.github.io/Scoliosis1K/static/resources/Scoliosis1k_release_agreement.pdf) and sending it to **[12331257@mail.sustech.edu.cn](mailto:12331257@mail.sustech.edu.cn)**.
 
-## Process from RAW dataset
+### Dataset Structure
+
+After decompression, you will get the following structure:
 
-### Preprocess the dataset (Optional)
+```
-Download the raw dataset from the [official link](https://zhouzi180.github.io/Scoliosis1K). You will get two compressed files, i.e. `Scoliosis1K-raw.zip`, and `Scoliosis1K-pkl.zip`.
+├── Scoliosis1K-sil-pkl
-We recommend using our provided pickle files for convenience, or process raw dataset into pickle by this command:
+│   ├── 00000                     # Identity
-```shell
+│   │   ├── Positive              # Class
-python datasets/pretreatment.py --input_path Scoliosis1K_raw --output_path Scoliosis1K-pkl
+│   │   │   ├── 000_180           # View
+│   │   │   └── 000_180.pkl       # Estimated Silhouette (PP-HumanSeg v2)
+│
+├── Scoliosis1K-pose-pkl
+│   ├── 00000                     # Identity
+│   │   ├── Positive              # Class
+│   │   │   ├── 000_180           # View
+│   │   │   └── 000_180.pkl       # Estimated 2D Pose (ViTPose)
+```
+
+### Processing from RAW Dataset (optional)
+
+If you prefer, you can process the raw dataset into `.pkl` format.
+
+```bash
+# For silhouette raw data
+python datasets/pretreatment.py --input_path=<path_to_raw_silhouettes> -output_path=<output_path>
+
+# For pose raw data
+python datasets/pretreatment.py --input_path=<path_to_raw_pose> -output_path=<output_path> --pose --dataset=OUMVLP
+```
+---
+
+## Training and Testing
+
+Before training or testing, modify the `dataset_root` field in
+`configs/sconet/sconet_scoliosis1k.yaml`.
+
+Then run the following commands:
+
+```bash
+# Training
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -m torch.distributed.launch --nproc_per_node=4 \
+opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase train --log_to_file
+
+# Testing
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -m torch.distributed.launch --nproc_per_node=4 \
+opengait/main.py --cfgs configs/sconet/sconet_scoliosis1k.yaml --phase test --log_to_file
+```
+
+---
+
+## Pose-to-Heatmap Conversion
+
+*From our paper: **Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening (MICCAI 2025)***
+
+```bash
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -m torch.distributed.launch --nproc_per_node=4 \
+datasets/pretreatment_heatmap.py \
+  --pose_data_path=<path_to_pose_pkl> \
+  --save_root=<output_path> \
+  --dataset_name=OUMVLP
 ```