Semi-supervised assessment of incomplete LV coverage in cardiac MRI using generative adversarial nets

L. Zhang, A. Gooya, A.F. Frangi

Research output: Chapter in Book/Conference proceedingChapterpeer-review

Abstract

Cardiac magnetic resonance (CMR) images play a growing role in diagnostic imaging of cardiovascular diseases. Ensuring full coverage of the Left Ventricle (LV) is a basic criteria of CMR image quality. Complete LV coverage, from base to apex, precedes accurate cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in large imaging cohorts. In this paper, we propose a novel semi-supervised method to check the coverage of LV from CMR images by using generative adversarial networks (GAN), we call it Semi-Coupled-GANs (SCGANs). To identify missing basal and apical slices in a CMR volume, a two-stage framework is proposed. First, the SCGANs generate adversarial examples and extract high-level features from the CMR images; then these image attributes are used to detect missing basal and apical slices. We constructed extensive experiments to validate the proposed method on UK Biobank with more than 6000 independent volumetric MR scans, which achieved high accuracy and robust results for missing slice detection, comparable with those of state of the art deep learning methods. The proposed method, in principle, can be adapted to other CMR image data for LV coverage assessment.
Original languageEnglish
Title of host publicationSimulation and Synthesis in Medical Imaging
Subtitle of host publicationSecond International Workshop, SASHIMI 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10, 2017, Proceedings
EditorsSotirios A. Tsaftaris, Ali Gooya, Alejandro F. Frangi, Jerry L. Prince
Place of PublicationCham
PublisherSpringer Cham
Pages61-68
Number of pages8
ISBN (Electronic)9783319681276
ISBN (Print)9783319681269
DOIs
Publication statusPublished - 30 Sept 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer
Volume10557
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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