Multi-Scale Fully Convolutional Network for Cardiac Left Ventricle Segmentation
This work addresses the problem of accurate and efficient diagnosis of cardiovascular diseases for medical professionals, but it is incremental as it builds on existing fully convolutional neural network methods.
The paper tackled cardiac left ventricle segmentation from MRI images by proposing a new fully convolutional neural network structure called MS-FCN, which achieved state-of-the-art results with Dice scores of 0.93 for endocardium and 0.96 for epicardium.
The morphological structure of left ventricle segmented from cardiac magnetic resonance images can be used to calculate key clinical parameters, and it is of great significance to the accurate and efficient diagnosis of cardiovascular diseases. Compared with traditional methods, the segmentation algorithms based on fully convolutional neural network greatly improve the accuracy of semantic segmentation. For the problem of left ventricular segmentation, a new fully convolutional neural network structure named MS-FCN is proposed in this paper. The MS-FCN network employs a multi-scale pooling module to ensure that the network maximises the feature extraction ability and uses a dense connectivity decoder to refine the boundaries of the object. Based on the Sunnybrook cine-MR dataset provided by the MICCAI 2009 challenge, numerical experiments demonstrate that our proposed model has obtained state-of-the-art segmentation results: the Dice score of our method reaches 0.93 on the endocardium, and 0.96 on the epicardium.