3D Convolutional Networks for Fully Automatic Fine-Grained Whole Heart Partition

  • Xin Yang
  • Cheng Bian
  • Lequan Yu
  • Dong Ni
  • Pheng-Ann Heng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10663)


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.



The work in this paper was supported by the grant from National Natural Science Foundation of China under Grant 61571304, a grant from Hong Kong Research Grants Council (Project no. CUHK 412513) and grants from the National Natural Science Foundation of China (Project No. 61233012 and No. 81601576).


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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongHong KongChina
  2. 2.National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Health Science CenterShenzhen UniversityShenzhenChina
  3. 3.Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology, Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

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