Official code for MICCAI2021. 'Contrastive Learning of Relative Position Regression for One-Shot Object Localization in 3D Medical Images'
You could download the processed dataset from: StructSeg task1 (Organ-at-risk segmentation from head & neck CT scans): BaiDu Yun or Google Drive and TCIA-Pancreas: BaiDu Yun or Google Drive into data/ and unzip them. For TCIA-Pancreas, please cite the original paper (Deeporgan: Multi-level deep convolutional networks for automated pancreas segmentation).
The training config is in config/train/, containing 4 files for coarse/fine & pancreas/head and neck dataset. You could change the parameters in configuration txt for your own experiments. More details could be found in config/train/readme.md.
For example, you could cd train then run python train_position.py ../config/train/train_position_pancreas_coarse.txt to train a coarse RPR model for TCIA-Pancreas dataset.
We provide the pre-trained Pnet_2 model for TCIA-Pancreas dataset. You could download the coarse model from Google Drive and the fine model from Google Drive. Both two models shoud be saved in weights/Pnet_2/.
For example, you could cd detection then run python c2f_detection.py ../config/test/test_c2f_pancreas_detection.txt for pancreas detection (the default setting requires the pre-trained models). You could change the parameters in configuration txt for your own experiments. More details could be found in config/test/readme.md.
We have fixed a bug in our original framework and increase the detection IOU of pancreas from 0.495 in the paper to 0.58.
If you find this research useful, please consider citing our work:
@inproceedings{lei2021contrastive,
title={Contrastive learning of relative position regression for one-shot object localization in 3D medical images},
author={Lei, Wenhui and Xu, Wei and Gu, Ran and Fu, Hao and Zhang, Shaoting and Zhang, Shichuan and Wang, Guotai},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={155--165},
year={2021},
organization={Springer}
}