3D convolutional networks for fully automatic fine-grained whole heart partition

Abstract

Segmenting cardiovascular volumes plays a crucial role for clinical applications, especially parsing the whole heart into fine-grained structures. However, conquering fuzzy boundaries and differentiating branchy structures in cardiovascular volume images still remain a challenging task. In this paper, we propose a general and fully automatic solution for fine-grained whole heart partition. The proposed framework originates from the 3D Fully Convolutional Network, and is reinforced in the following aspects: (1) By inheriting the knowledge from a pre-trained C3D Network, our network launches with a good initialization and gains capabilities in coping with overfitting. (2) We triggered several auxiliary loss functions on shallow layers to promote gradient flow and thus alleviate the training difficulties associated with deep neural networks. (3) Considering the obvious volume imbalance among different substructures, we introduced a Multi-class Dice Similarity Coefficient based metric to efficiently balance the training for all classes. We evaluated our method on the MM-WHS Challenge 2017 datasets. Extensive experimental results demonstrated the promising performance of our method. Our framework achieves promising results across different modalities and is general to be referred in other volumetric segmentation tasks.