sheng-xc / mscmr_seg

This is a PyTorch implementation based on the MICCAI paper by Qian Yue et al. "Cardiac Segmentation from LGE MRI Using Deep Neural Network from LGE MRI Using Deep Neural Network Incoporating Shape and Spatial Priors".

LGE-CMR segmentation

This is a PyTorch implementation based on the MICCAI paper by Qian Yue et al. on LGE-CMR segmentation. UNet, SRNN, SCN and SRSCN are performed for the segmentation task.

Introduction

Automatic cardiac segmentation from LGE-CMR is of great clinical value. In [3], Yue et al. proposed SRSCN, a U-Net based method incorporating additional modules for shape reconstruction and spatial constraint. The pipeline from their paper summarizes the model. For more details, please refer to [3].

Training

In this project, we trained the basic UNet, SRNN, SCN and SRSCN on MSCMR dataset, which is available upon registration.

python main.py --path "data_path" --batch_size 8 --dim 240 --lr 1e-4 --threshold 0.65 --end_epoch 30

The data path is organized as follows:

data/
  -- image files & gt files
  -- train.txt (with each line: image_path gd_path z_index)
  -- validation.txt (with each line: image_path gd_path z_index)
  -- test.txt (with each line: image_path dx dy dz)

Pretrained models with bathsize = 8 and epoch = 30 are stored in checkpoints/model_name.

Prediction

To use the models for segmentation, please prepare test.txt in the data path as described above and type:

python predict.py --load_path checkpoints/"model name" --predict_mode multiple --threshold 0.6 --dim 240

Citations

[1]Xiahai Zhuang: Multivariate mixture model for myocardial segmentation combining multi-source images. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(12), 2933–2946, 2019

[2]Xiahai Zhuang: Multivariate mixture model for cardiac segmentation from multi-sequence MRI. MICCAI 2016, 581–588, Springer, 2016

[3]Q Yue, X Luo, Q Ye, L, Xu, X Zhuang. Cardiac Segmentation from LGE MRI Using Deep Neural Network Incorporating Shape and Spatial Priors. MICCAI 2019, LNCS 11765, pp. 559-567, 2019. https://github.com/xzluo97/LGE_SRSCN