# H2ASeg: Hierarchical Interaction and Weighting Network for Tumor Segmentation in PET/CT images

## Paper

- This project is the open source code of H2ASeg

## Usage:

- Datasets
  - [Automated Lesion Segmentation in PET/CT Challenge](https://autopet-ii.grand-challenge.org/dataset/)
  - [MICCAI Hecktor 2022 Challenge](https://hecktor.grand-challenge.org/Data/)

- Train
```
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:

```setup
pip install -r requirements.txt
```

## Dataset

- [Automated Lesion Segmentation in PET/CT Challenge](https://autopet-ii.grand-challenge.org/dataset/)
- [MICCAI Hecktor 2022 Challenge](https://hecktor.grand-challenge.org/Data/)

## Preprocessing

A brief description of the preprocessing method

- registration
- intensity normalization

Running the data preprocessing code:

```python
python registration.py
python preprocessing.py
```

## Training

To train the model(s) in the paper, run this command:

```python
python train.py
```

## Inference and Evaluation

To infer the testing cases and compute the evaluation metrics, run this command:

```python
python inference.py
```

## Results

Our method achieves the following performance on [Automated Lesion Segmentation in PET/CT Challenge](https://autopet-ii.grand-challenge.org/dataset/) and [MICCAI Hecktor 2022 Challenge](https://hecktor.grand-challenge.org/Data/)

| Dateset name | Model name       |  DICE  | 95% Hausdorff Distance |
| ------------ | ---------------- | :----: | :--------------------: |
| AutoPET-II   | H2ASeg           | 60.03% |         63.09          |
| Hecktor2022  | H2ASeg           | 59.69% |         131.92         |
# H2ASeg_JinPLU
# H2ASeg_JinPLU