3D Convolutional Networks for Fully Automatic Fine-Grained Whole Heart Partition
Refereed conference paper presented and published in conference proceedings

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AbstractSegmenting 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.
All Author(s) ListXin Yang, Cheng Bian, Lequan Yu, Dong Ni, Pheng-Ann Heng
Name of Conference8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017
Start Date of Conference10/09/2017
End Date of Conference14/09/2017
Place of ConferenceQuebec
Country/Region of ConferenceCanada
Proceedings TitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume Number10663
Pages181 - 189
LanguagesEnglish-United States

Last updated on 2021-10-05 at 23:49