CPMNetv2: A Simpler and Stronger 3D Object Detection Benchmark in Medical Image

Benchmarks

Luna

Methods	1/8	1/4	1/2	1	2	4	8	CPM	TTA
Dou et al. (2017a)	0.692	0.745	0.819	0.865	0.906	0.933	0.946	0.839	False
Zhu et al. (2018)	0.692	0.769	0.824	0.865	0.893	0.917	0.933	0.842	False
Wang et al. (2018)	0.676	0.776	0.879	0.949	0.958	0.958	0.958	0.878	False
Ding et al. (2017)	0.748	0.853	0.887	0.922	0.938	0.944	0.946	0.891	False
Khosravan et al. (2018)	0.709	0.836	0.921	0.953	0.953	0.953	0.953	0.897	False
Liu et al. (2019)	0.848	0.876	0.905	0.933	0.943	0.957	0.970	0.919	False
Song et al. (2020)	0.723	0.838	0.887	0.911	0.928	0.934	0.948	0.881	False
nnDetection v0.1	0.812	0.885	0.927	0.950	0.969	0.979	0.985	0.930	True
CPMNet v2 (ours)	0.896	0.939	0.961	0.962	0.972	0.981	0.981	0.956	False
Methods with FPR^*	--	--	--	--	--	--	--	--	--
Cao et al. (2020) + FPR	0.848	0.899	0.925	0.936	0.949	0.957	0.960	0.925	False
Liu et al. (2019) + FPR	0.904	0.914	0.933	0.957	0.971	0.971	0.971	0.952	False

^* False Positive Reduction Network (second stage).

Private data

Both aneurysm detection in TOF-MRA and rib fracture detection in CT Scans, our method achieved better results (w.o TTA) than nndetection (w. TTA). You are welcome to use it in other public data sets and can update its performance to me.

Installation

Create conda env

conda create -n env_name python==3.7

Install requirements

conda install pytorch torchvision torchaudio pytorch-cuda=11.6 -c pytorch -c nvidia
pip install SimpleITK==2.2.1 pandas==1.3.5 scikit-image==0.19.3 scipy==1.7.3

Train

bash train_xxx.sh

Note: args.num_sam depend on the average number of instance (lesion) in per sample (N), suggest you set to 2N. The real batch size is (args.batch_size * args.num_sam), be careful with your GPU memory.

If you use CPMNetv2, please cite our papers:

{@inproceedings{song2020cpm,
title={CPM-Net: A 3D Center-Points Matching Network for Pulmonary Nodule Detection in CT Scans},
author={Song, Tao and Chen, Jieneng and Luo, Xiangde and Huang, Yechong and Liu, Xinglong and Huang, Ning and Chen, Yinan and Ye, Zhaoxiang and Sheng, Huaqiang and Zhang, Shaoting and others},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={550--559},
year={2020},
organization={Springer}
}

@article{luo2021scpmnet,
title={SCPM-Net: An anchor-free 3D lung nodule detection network using sphere representation and center points matching},
author={Luo, Xiangde and Song, Tao and Wang, Guotai and Chen, Jieneng and Chen, Yinan and Li, Kang and Metaxas, Dimitris N and Zhang, Shaoting},
journal={Medical Image Analysis},
volume={75},
pages={102287},
year={2022},
publisher={Elsevier}
}

zunzhumu/CPMNetv2