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Recurrent neural networks for aortic image sequence segmentation with sparse annotations

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Title: Recurrent neural networks for aortic image sequence segmentation with sparse annotations
Authors: Bai, W
Suzuki, H
Qin, C
Tarroni, G
Oktay, O
Matthews, PM
Rueckert, D
Item Type: Conference Paper
Abstract: Segmentation of image sequences is an important task in medical image analysis, which enables clinicians to assess the anatomy and function of moving organs. However, direct application of a segmentation algorithm to each time frame of a sequence may ignore the temporal continuity inherent in the sequence. In this work, we propose an image sequence segmentation algorithm by combining a fully convolutional network with a recurrent neural network, which incorporates both spatial and temporal information into the segmentation task. A key challenge in training this network is that the available manual annotations are temporally sparse, which forbids end-to-end training. We address this challenge by performing non-rigid label propagation on the annotations and introducing an exponentially weighted loss function for training. Experiments on aortic MR image sequences demonstrate that the proposed method significantly improves both accuracy and temporal smoothness of segmentation, compared to a baseline method that utilises spatial information only. It achieves an average Dice metric of 0.960 for the ascending aorta and 0.953 for the descending aorta.
Issue Date: 16-Sep-2018
Date of Acceptance: 25-May-2018
URI: http://hdl.handle.net/10044/1/64136
DOI: https://dx.doi.org/10.1007/978-3-030-00937-3_67
ISBN: 9783030009366
ISSN: 0302-9743
Publisher: Springer Nature Switzerland AG
Start Page: 586
End Page: 594
Journal / Book Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 11073 LNCS
Copyright Statement: © 2018 Springer-Verlag. The final publication is available at Springer via https://dx.doi.org/10.1007/978-3-030-00937-3_67
Sponsor/Funder: Imperial College Healthcare NHS Trust- BRC Funding
Engineering & Physical Science Research Council (EPSRC)
Funder's Grant Number: RD410
Conference Name: International Conference On Medical Image Computing & Computer Assisted Intervention
Keywords: cs.CV
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-09-16
Finish Date: 2018-09-20
Conference Place: Granada, Spain
Online Publication Date: 2018-09-13
Appears in Collections:Computing
Department of Medicine (up to 2019)