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2018 | OriginalPaper | Chapter

Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes

Authors : Vanya V. Valindria, Ioannis Lavdas, Juan Cerrolaza, Eric O. Aboagye, Andrea G. Rockall, Daniel Rueckert, Ben Glocker

Published in: Machine Learning in Medical Imaging

Publisher: Springer International Publishing

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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.

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Title: Small Organ Segmentation in Whole-Body MRI Using a Two-Stage FCN and Weighting Schemes
Authors: Vanya V. Valindria
Ioannis Lavdas
Juan Cerrolaza
Eric O. Aboagye
Andrea G. Rockall
Daniel Rueckert
Ben Glocker
Publisher: Springer International Publishing
Book: Machine Learning in Medical Imaging
Print ISBN: 978-3-030-00918-2

Electronic ISBN: 978-3-030-00919-9

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00919-9_40

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