H2ASeg: Hierarchical Interaction and Weighting Network for Tumor Segmentation in PET/CT images
Paper
- This project is the open source code of H2ASeg
Usage:
python -u train.py
Code checklist for machine learning-based MICCAI papers
Environments and Requirements
- Ubuntu version: Ubuntu 20.04.6 LTS
- CPU: AMD EPYC 7763 64-Core Processor
- GPU: NVIDIA GeForce RTX 4090
- CUDA: 12.2
- python: 3.10.16
To install requirements:
pip install -r requirements.txt
Dataset
Preprocessing
A brief description of the preprocessing method
- registration
- intensity normalization
Running the data preprocessing code:
python registration.py
python preprocessing.py
Training
To train the model(s) in the paper, run this command:
python train.py
Inference and Evaluation
To infer the testing cases and compute the evaluation metrics, run this command:
python inference.py
Results
Our method achieves the following performance on Automated Lesion Segmentation in PET/CT Challenge and MICCAI Hecktor 2022 Challenge
| Dateset name | Model name | DICE | 95% Hausdorff Distance |
|---|---|---|---|
| AutoPET-II | H2ASeg | 60.03% | 63.09 |
| Hecktor2022 | H2ASeg | 59.69% | 131.92 |