qiaosibo / clas-pytorch

Temporal-consistent segmentation of echocardiography (MICCAI 2020 early accept)

CLAS-pytorch (Debugging)

This is an official implementation of "Temporal-consistent segmentation of echocardiography with co-learning from appearance and shape" (MICCAI 2020 early accept, Oral)

[Paper] [Slide]

Dataset / Challenge

Cardiac Acquisitions for Multi-structure Ultrasound Segmentation, CAMUS

Method (CLAS)

Results (SOTA)

• Visualization of temporal consistency

• Cardiac (endocardium, epicardium, and left atrium) segmentation

Methods	Endo			Epi			LA
ED phase	Dice	HD	MAD	Dice	HD	MAD	Dice	HD	MAD
U-Net	0.936	5.3	1.7	0.956	5.2	1.7	0.889	5.7	2.2
ACNNs	0.936	5.6	1.7	0.953	5.9	1.9	0.881	6.0	2.3
CLAS	0.947	4.6	1.4	0.961	4.8	1.5	0.902	5.2	1.9
ES phase	Dice	HD	MAD	Dice	HD	MAD	Dice	HD	MAD
U-Net	0.912	5.5	1.7	0.946	5.7	1.9	0.918	5.3	2.0
ACNNs	0.913	5.6	1.7	0.945	5.9	2.0	0.911	5.8	2.2
CLAS	0.929	4.6	1.4	0.955	4.9	1.6	0.927	4.8	1.8

Note: ED & ES: end-diastole and end-systole phases; HD: Hausdorff distance; MAD: Mean absolute distance

• Volumes (EDV & ESV) and ejection fraction (EF) estimation

Methods	EDV			ESV			EF
	corr	bias(ml)	std	corr	bias(ml)	std	corr	bias(%)	std
U-Net	0.926	7.2	15.6	0.960	4.4	10.2	0.845	0.1	7.3
ACNNs	0.928	2.8	15.5	0.954	2.0	10.1	0.807	0.3	8.3
CLAS	0.958	-0.7	15.1	0.979	-0.0	8.4	0.926	-0.1	6.7

Note: corr: Pearson Correlation Coefficient

Usage

• Requirement

pip install torch matplotlib scikit-image opencv-python SimpleITK scipy imageio pillow

• Download dataset with following storage format:

./data/training/patient-id/*
./data/testing/patient-id/*

• Run

Read and preprocess training data

python ./utils/read_data.py

After preprocess, A2C/A4C.npy with size (450, 10, 256, 256) and A2C_gt/A4C_gt.npy with size (450, 2, 256, 256) were generated for apical two-chamber and four-chamber views in the folder "./data/". 450 means the number of patients for training, 10 means the number of sampling frames for each sequences, and only end-diastole and end-systole frames have ground truths.

Training

python train.py

Testing

python test.py

Citation

Please cite our paper if you find anything helpful:

@InProceedings{CLAS,
author={Wei, Hongrong and Cao, Heng and Cao, Yiqin and Zhou, Yongjin and Xue, Wufeng and Ni, Dong and Li, Shuo},
title={Temporal-Consistent Segmentation of Echocardiography with Co-learning from Appearance and Shape},
booktitle={Medical Image Computing and Computer Assisted Intervention -- MICCAI 2020},
year={2020},
publisher={Springer International Publishing},
pages={623--632},
isbn={978-3-030-59713-9}
}