3D deeply-supervised U-net based whole heart segmentation

Qianqian Tong, Munan Ning, Weixin Si, Xiangyun Liao, Jing Qin

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

42 Citations (Scopus)


Accurate whole-heart segmentation from multi-modality medical images (MRI, CT) plays an important role in many clinical applications, such as precision surgical planning and improvement of diagnosis and treatment. This paper presents a deeply-supervised 3D U-Net for fully automatic whole-heart segmentation by jointly using the multi-modal MRI and CT images. First, a 3D U-Net is employed to coarsely detect the whole heart and segment its region of interest, which can alleviate the impact of surrounding tissues. Then, we artificially enlarge the training set by extracting different regions of interest so as to train a deep network. We perform voxel-wise whole-heart segmentation with the end-to-end trained deeply-supervised 3D U-Net. Considering that different modality information of the whole heart has a certain complementary effect, we extract multi-modality features by fusing MRI and CT images to define the overall heart structure, and achieve final results. We evaluate our method on cardiac images from the multi-modality whole heart segmentation (MM-WHS) 2017 challenge.
Original languageEnglish
Title of host publicationStatistical Atlases and Computational Models of the Heart
Subtitle of host publicationACDC and MMWHS Challenges - 8th International Workshop, STACOM 2017, Revised Selected Papers
EditorsOlivier Bernard, Pierre-Marc Jodoin, Xiahai Zhuang, Guang Yang, Alistair Young, Maxime Sermesant, Alain Lalande, Mihaela Pop
PublisherSpringer Verlag
Number of pages9
ISBN (Print)9783319755403
Publication statusPublished - 1 Jan 2018
Event8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017 - Quebec City, Canada
Duration: 10 Sept 201714 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10663 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017
CityQuebec City


  • 3D deeply-supervised U-Net
  • Multi-modal cardiac images
  • Whole heart segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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