DianaNerualNetwork / PANet

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

Prior Attention Network for Multi-Lesion Segmentation in Medical Images

🎉 Our work has been accepted by IEEE Transactions on Medical Imaging

Authors:

Xiangyu Zhao[1][2][3], Peng Zhang[1][2], Fan Song[1][2], Chenbin Ma[1][2], Guangda Fan[1][2], Yangyang Sun[1][2], Youdan Feng[1][2], Guanglei Zhang[1][2]*

Institution:

[1] School of Biological Science and Medical Engineering, Beihang University, Beijing, China
[2] Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China
[3] School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
*Corresponding Author: Guanglei Zhang

manuscript link:

• https://arxiv.org/abs/2110.04735 (preprint on arXiv, early version)
• https://ieeexplore.ieee.org/document/9852260 (IEEE TMI, journal version)

This repo contains the implementation of 3D segmentation of BraTS 2020 with the proposed Prior Attention Network.
If you use our code, please cite the paper:

@ARTICLE{9852260,
author={Zhao, Xiangyu and Zhang, Peng and Song, Fan and Ma, Chenbin and Fan, Guangda and Sun, Yangyang and Feng, Youdan and Zhang, Guanglei},
journal={IEEE Transactions on Medical Imaging},
title={Prior Attention Network for Multi-Lesion Segmentation in Medical Images},
year={2022},
volume={},
number={},
pages={1-1},
doi={10.1109/TMI.2022.3197180}}

Methods

In this paper we propose a novel Prior Attention Network with intermediate supervision, parameterized skip connections and deep supervision strategy to address multi-lesion segmentation problems in medical images.

Network Topology

Attention Guiding Decoder

Results

Quantitative Results

Qualitative Results

Ablation Analysis

Usage

Data Preparation

Please download BraTS 2020 data according to https://www.med.upenn.edu/cbica/brats2020/data.html.
Unzip downloaded data at ./data folder (please create one) and remove all the csv files in the folder, or it will cause errors.

Pretrained Checkpoint

We provide ckpt download via Google Drive or Baidu Netdisk. Please download the checkpoint from the url below:

Google Drive

url: https://drive.google.com/file/d/1OwdKnM51rDvF3UiQDbcCWlPcYdc94-_O/view?usp=sharing

Baidu Netdisk

url：https://pan.baidu.com/s/14qM2k46mFnzT2RmI3sWcSw
extraction code (提取码)：0512

Training

For default training configuration, we use patch-based training pipeline and use Adam optimizer. Deep supervision is utilized to facilitate convergence.

Train and validate on BraTS training set

python train.py --model panet --patch_test --ds

Training on the entire BraTS training set

python train.py --model panet --patch_test --ds --trainset

Breakpoint continuation for training

python train.py --model panet --patch_test --ds -c CKPT

this will load the pretrained weights as well as the status of optimizer and scheduler.

PyTorch-native AMP training

python train.py --model panet --patch_test --ds --mixed

if the training is too slow, please enable CUDNN benchmark by adding --benchmark but it will slightly affects the reproducibility.

Inference

For default inference configuration, we use patch-based pipeline.

python inference.py --model panet --patch_test --validation -c CKPT

Our Results

If you have trouble reproducing our results, we also provided the download link of our vanilla results inferred on BraTS 2020 validation set. You may download this via Google drive or Baidu Netdisk.

Google Drive

url: https://drive.google.com/file/d/1Vq_NGXdiZSl2ez4PP9DhVQc3JL02TU8C/view?usp=sharing

Baidu Netdisk

url：https://pan.baidu.com/s/1jyiJrIs3CZHDrrEY_gycKg?pwd=0512
extraction code (提取码)：0512