Myocardial Infarction Segmentation From Late Gadolinium Enhancement MRI By Neural Networks and Prior Information

In this paper, we propose an automatic myocardial infarction segmentation framework from Delayed Enhancement cardiac MRI (DE-MRI) using a convolutional neural network (CNN) and prior information-based post-treatments. The work was conducted on our DE-MRI dataset, which is collected from daily clinical practice. 195 cases of DE-MRI examinations constitute this dataset, including on average 7 images per case with manually drawn contours by an expert. The objective is to automatically segment myocardial infarctions on both healthy and pathological images in the dataset. In the proposed framework, a downsampling-upsampling segmentation CNN firstly generates high recall segmentations of myocardial infarction from left ventricle DE-MR images, then the proposed prior information-based post-processing method identifies and removes false-positive segmentations from the CNN's prediction. To obtain a high recall prediction, two U-NET like semantic segmentation networks are investigated: CE-NET and its backbone with Dice loss and Stochastic Gradient Descent (SGD) using a batch size of value 1. The prior information-based post-processing evaluates every single contour in the CNN's segmentations: region features in each contour are compared to criteria which are firstly estimated based on the training set images and eventually fine-tuned based on the validation set images. All non-conforming contours are removed from the predictions to improve the accuracy of the segmentation. Combining the high recall networks and prior postprocessing information, we achieve segmentation results comparable to those produced by human experts.