Tutorial for Scoliosis1K

Download the Scoliosis1K Dataset

You can download the dataset from the official website. The dataset is provided as four compressed files:

Scoliosis1K-sil-raw.zip
Scoliosis1K-sil-pkl.zip
Scoliosis1K-pose-raw.zip
Scoliosis1K-pose-pkl.zip

We recommend using the provided pickle (.pkl) files for convenience. Decompress them with the following commands:

unzip -P <password> Scoliosis1K-sil-pkl.zip
unzip -P <password> Scoliosis1K-pose-pkl.zip

Note

: The <password> can be obtained by signing the release agreement and sending it to 12331257@mail.sustech.edu.cn.

Dataset Structure

After decompression, you will get the following structure:

├── Scoliosis1K-sil-pkl
│   ├── 00000                     # Identity
│   │   ├── Positive              # Class
│   │   │   ├── 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.

# 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:

# 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)

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

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