Class-Balanced Deep Neural Network for Automatic Ventricular Structure Segmentation

• Published in: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges Vol. 10663; pp. 152 - 160
• Main Authors: Yang, Xin, Bian, Cheng, Yu, Lequan, Ni, Dong, Heng, Pheng-Ann
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• ISBN: 9783319755403; 3319755404
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-75541-0_16

Segmenting ventricular structures from cardiovascular MR scan is important for quantitative evaluation of heart. Manual delineation is time-consuming and tedious and lack of reproductivity. Considering MR image quality, heart variance, spatial inconsistency and motion artifacts during scanning, it is still a non-trivial task for automatic segmentation methods. In this paper, we propose a general and fully automatic solution to concurrently segment three important ventricular structures. Rooting in the deep learning trend, our method starts from 3D Fully Convolutional Network (3D FCN). We then enhance the 3D FCN with two well-verified blocks: (1) we conduct transfer learning between a pre-trained C3D model and our 3D FCN to get good initialization and thus suppress overfitting. (2) since boosting the gradient flow in network is beneficial to promote segmentation performance, we attach several auxiliary loss functions so as to expose early layers to better supervision. Because the volume size imbalance among different ventricular structures often biases the training of our 3D FCN, to this end, we investigate the capacity of different loss functions and propose a Multi-class Dice Similarity Coefficient (mDSC) based loss function to re-weight the training for all classes. We verified our method, especially the significance of mDSC, on the Automated Cardiac Diagnosis Challenge 2017 datasets for MR image segmentation. Extensive experimental results demonstrate the promising performance of our method.