Small organ segmentation in whole-body MRI using a two-stage FCN and weighting schemes

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Title: Small organ segmentation in whole-body MRI using a two-stage FCN and weighting schemes
Authors: Valindria, V
Lavdas, I
Cerrolaza, J
Aboagye, EO
Rockall, A
Rueckert, D
Glocker, B
Item Type: Conference Paper
Abstract: Accurate and robust segmentation of small organs in whole-body MRI is difficult due to anatomical variation and class imbalance. Recent deep network based approaches have demonstrated promising performance on abdominal multi-organ segmentations. However, the performance on small organs is still suboptimal as these occupy only small regions of the whole-body volumes with unclear boundaries and variable shapes. A coarse-to-fine, hierarchical strategy is a common approach to alleviate this problem, however, this might miss useful contextual information. We propose a two-stage approach with weighting schemes based on auto-context and spatial atlas priors. Our experiments show that the proposed approach can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.
Issue Date: 15-Sep-2018
Date of Acceptance: 18-Jul-2018
ISBN: 978-3-030-00918-2
ISSN: 0302-9743
Publisher: Springer Verlag
Start Page: 346
End Page: 354
Journal / Book Title: Machine Learning in Medical Imaging
Volume: LNCS, 11046
Copyright Statement: © Springer Nature Switzerland AG 2018. The final publication is available at Springer via
Sponsor/Funder: Cancer Research UK
Imperial College Healthcare NHS Trust- BRC Funding
National Institute for Health Research
Commission of the European Communities
Funder's Grant Number: 10337
RDC04 79560
H2020 - 757173
Conference Name: International Workshop on Machine Learning in Medical Imaging (MLMI) 2018
Keywords: cs.CV
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status: Published
Start Date: 2018-09-16
Conference Place: Granada, Spain
Online Publication Date: 2018-09-15
Appears in Collections:Faculty of Engineering
Division of Surgery
Division of Cancer
Faculty of Medicine

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