| # H2ASeg: Hierarchical Interaction and Weighting Network for Tumor Segmentation in PET/CT images
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| ## Paper
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| - This project is the open source code of H2ASeg
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| ## Usage:
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| - Datasets
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| - [Automated Lesion Segmentation in PET/CT Challenge](https://autopet-ii.grand-challenge.org/dataset/)
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| - [MICCAI Hecktor 2022 Challenge](https://hecktor.grand-challenge.org/Data/)
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| - Train
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| ```
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| python -u train.py
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| ```
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| # Code checklist for machine learning-based MICCAI papers
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| ## Environments and Requirements
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| - Ubuntu version: Ubuntu 20.04.6 LTS
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| - CPU: AMD EPYC 7763 64-Core Processor
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| - GPU: NVIDIA GeForce RTX 4090
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| - CUDA: 12.2
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| - python: 3.10.16
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| To install requirements:
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| ```setup
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| pip install -r requirements.txt
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| ```
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| ## Dataset
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| - [Automated Lesion Segmentation in PET/CT Challenge](https://autopet-ii.grand-challenge.org/dataset/)
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| - [MICCAI Hecktor 2022 Challenge](https://hecktor.grand-challenge.org/Data/)
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| ## Preprocessing
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| A brief description of the preprocessing method
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| - registration
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| - intensity normalization
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| Running the data preprocessing code:
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| ```python
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| python registration.py
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| python preprocessing.py
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| ```
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| ## Training
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| To train the model(s) in the paper, run this command:
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| ```python
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| python train.py
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| ```
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| ## Inference and Evaluation
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| To infer the testing cases and compute the evaluation metrics, run this command:
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| ```python
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| python inference.py
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| ```
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| ## Results
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| 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/)
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| | Dateset name | Model name | DICE | 95% Hausdorff Distance |
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| | ------------ | ---------------- | :----: | :--------------------: |
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| | AutoPET-II | H2ASeg | 60.03% | 63.09 |
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| | Hecktor2022 | H2ASeg | 59.69% | 131.92 |
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| # H2ASeg_JinPLU |
| # H2ASeg_JinPLU |
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|
