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TeTrIS: template transformer networks for image segmentation with shape priors

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Title: TeTrIS: template transformer networks for image segmentation with shape priors
Authors: Lee, M
Petersen, K
Pawlowski, N
Glocker, B
Schaap, M
Item Type: Journal Article
Abstract: In this paper we introduce and compare different approaches for incorporating shape prior information into neural network based image segmentation. Specifically, we introduce the concept of template transformer networks where a shape template is deformed to match the underlying structure of interest through an end-to-end trained spatial transformer network. This has the advantage of explicitly enforcing shape priors and is free of discretisation artefacts by providing a soft partial volume segmentation. We also introduce a simple yet effective way of incorporating priors in state-of-the-art pixel-wise binary classification methods such as fully convolutional networks and U-net. Here, the template shape is given as an additional input channel, incorporating this information significantly reduces false positives. We report results on synthetic data and sub-voxel segmentation of coronary lumen structures in cardiac computed tomography showing the benefit of incorporating priors in neural network based image segmentation.
Issue Date: 1-Nov-2019
Date of Acceptance: 12-Mar-2019
URI: http://hdl.handle.net/10044/1/69113
DOI: 10.1109/TMI.2019.2905990
ISSN: 0278-0062
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 2596
End Page: 2606
Journal / Book Title: IEEE Transactions on Medical Imaging
Volume: 38
Issue: 11
Copyright Statement: © 2019 IEEE. This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/
Sponsor/Funder: HeartFlow Inc
Funder's Grant Number: PO 1194
Keywords: Science & Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Neural networks
Image segmentation
Deformable models
Task analysis
shape priors
neural networks
template deformation
image registration
08 Information and Computing Sciences
09 Engineering
Nuclear Medicine & Medical Imaging
Publication Status: Published
Online Publication Date: 2019-03-22
Appears in Collections:Faculty of Engineering