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

替代計量分析
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其它資訊
摘要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.
著者Xin Yang, Cheng Bian, Lequan Yu, Dong Ni, Pheng-Ann Heng
會議名稱8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017
會議開始日10.09.2017
會議完結日14.09.2017
會議地點Quebec
會議國家/地區加拿大
會議論文集題名Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
出版年份2018
卷號10663
出版社Springer
頁次181 - 189
國際標準書號978-3-319-75540-3
電子國際標準書號978-3-319-75541-0
國際標準期刊號0302-9743
語言美式英語

上次更新時間 2020-11-07 於 02:58