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Exploring Cycle Consistency Learning in Interactive Volume Segmentation

PyTorch implementation for paper Exploring Cycle Consistency Learning in Interactive Volume Segmentation.

Qin Liu1, Meng Zheng2, Benjamin Planche2, Zhongpai Gao2, Terrence Chen2, Marc Niethammer1, Ziyan Wu2
1UNC-Chapel Hill, 2United Imaging Intelligence

drawing

This repository also contains our MICCAI 2022 paper iSegFormer in branch v1.0.

iSegFormer: Interactive Image Segmentation via Transformers with Application to 3D Knee MR Images. (MICCAI 2022)
Qin Liu, Zhenlin Xu, Yining Jiao, Marc Niethammer
UNC-Chapel Hill

Installation

The code is tested with python=3.9, torch=1.12.0, and torchvision=0.13.0 on an A6000 GPU.

git clone https://github.com/uncbiag/iSegFormer
cd iSegFormer

Now, create a new conda environment and install required packages accordingly.

conda create -n isegformer python=3.9
conda activate isegformer
pip3 install -r requirements.txt

Getting Started

First, download AbdomenCT-1K dataset and model weights. AbdomenCT-1K will be saved in the data folder; model weights will be saved in the saves folder.

python download.py

Unzip the AbdomenCT-1K in the data folder accordingly. Then run a demo:

./run_demo.sh

You will get a GUI as below:

drawing

Training

To finetune an STCN model on AbdomenCT-1K with cycle consistency loss:

./run_train_stcn_with_cycle.sh

To finetune an STCN model on AbdomenCT-1K without cycle consistency loss:

./run_train_stch_without_cycle.sh

Evaluation

To evaluate a trained model on AbdomenCT-1K:

./run_eval_stcn.sh

Acknowledgement

We sincerely thank STM, STCN, MiVOS, AbdomenCT-1K for providing their wonderful code to the community!

Citation

@article{liu2023exploring,
  title={Exploring Cycle Consistency Learning in Interactive Volume Segmentation},
  author={Liu, Qin and Zheng, Meng and Planche, Benjamin and Gao, Zhongpai and Chen, Terrence and Niethammer, Marc and Wu, Ziyan},
  journal={arXiv preprint arXiv:2303.06493},
  year={2023}
}