Multi-planar deep segmentation networks for cardiac substructures from MRI and CT

Aliasghar Mortazi, Jeremy Burt, Ulas Bagci*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

22 Scopus citations

Abstract

Non-invasive detection of cardiovascular disorders from radiology scans requires quantitative image analysis of the heart and its substructures. There are well-established measurements that radiologists use for diseases assessment such as ejection fraction, volume of four chambers, and myocardium mass. These measurements are derived as outcomes of precise segmentation of the heart and its substructures. The aim of this paper is to provide such measurements through an accurate image segmentation algorithm that automatically delineates seven substructures of the heart from MRI and/or CT scans. Our proposed method is based on multi-planar deep convolutional neural networks (CNN) with an adaptive fusion strategy where we automatically utilize complementary information from different planes of the 3D scans for improved delineations. For CT and MRI, we have separately designed three CNNs (the same architectural configuration) for three planes, and have trained the networks from scratch for voxel-wise labeling for the following cardiac structures: myocardium of left ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We have evaluated the proposed method with 4-fold-cross-validation on the multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. A precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for CT and MR images, respectively. Cardiac CT volume was segmented in about 50 s, with cardiac MRI segmentation requiring around 17 s with multi-GPU/CUDA implementation.

Original languageEnglish (US)
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
Pages199-206
Number of pages8
ISBN (Print)9783319755403
DOIs
StatePublished - 2018
Externally publishedYes
Event8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017 - Quebec City, Canada
Duration: Sep 10 2017Sep 14 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

Conference

Conference8th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2017, Held in Conjunction with MICCAI 2017
Country/TerritoryCanada
CityQuebec City
Period9/10/179/14/17

Keywords

  • Cardiac magnetic resonance imaging
  • Cardiovascular disorders
  • Computed tomography
  • Convolutional neural network
  • Whole heart segmentation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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