The deep Poincare map: A novel approach for left ventricle segmentation
File(s)
Accepted version
Author(s)
Type
Conference Paper
Abstract
Precise segmentation of the left ventricle (LV) within cardiac MRI images is a prerequisite for the quantitative measurement of heart function. However, this task is challenging due to the limited availability of labeled data and motion artifacts from cardiac imaging. In this work, we present an iterative segmentation algorithm for LV delineation. By coupling deep learning with a novel dynamic-based labeling scheme, we present a new methodology where a policy model is learned to guide an agent to travel over the image, tracing out a boundary of the ROI – using the magnitude difference of the Poincaré map as a stopping criterion. Our method is evaluated on two datasets, namely the Sunnybrook Cardiac Dataset (SCD) and data from the STACOM 2011 LV segmentation challenge. Our method outperforms the previous research over many metrics. In order to demonstrate the transferability of our method we present encouraging results over the STACOM 2011 data, when using a model trained on the SCD dataset.
Editor(s)
Frangi, AF
Schnabel, JA
Davatzikos, C
AlberolaLopez, C
Fichtinger, G
Date Issued
2018-09-13
Date Acceptance
2018-09-01
Citation
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV, 2018, 11073, pp.561-568
ISBN
978-3-030-00936-6
ISSN
0302-9743
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Start Page
561
End Page
568
Journal / Book Title
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2018, PT IV
Volume
11073
Copyright Statement
© Springer Nature Switzerland AG 2018.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000477769100064&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Source
21st International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Subjects
Science & Technology
Technology
Computer Science, Theory & Methods
Imaging Science & Photographic Technology
Computer Science
LEVEL SET
MODEL
Publication Status
Published
Start Date
2018-09-16
Finish Date
2018-09-20
Coverage Spatial
Granada, SPAIN
Date Publish Online
2018-09-13