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Multi-atlas segmentation using partially annotated data: methods and annotation strategies

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Title: Multi-atlas segmentation using partially annotated data: methods and annotation strategies
Authors: Koch, LM
Rajchl, M
Bai, W
Baumgartner, CF
Tong, T
Passerat-Palmbach, J
Aljabar, P
Rueckert, D
Item Type: Journal Article
Abstract: Multi-atlas segmentation is a widely used tool in medical image analysis, providing robust and accurate results by learning from annotated atlas datasets. However, the availability of fully annotated atlas images for training is limited due to the time required for the labelling task. Segmentation methods requiring only a proportion of each atlas image to be labelled could therefore reduce the workload on expert raters tasked with annotating atlas images. To address this issue, we first re-examine the labelling problem common in many existing approaches and formulate its solution in terms of a Markov Random Field energy minimisation problem on a graph connecting atlases and the target image. This provides a unifying framework for multi-atlas segmentation. We then show how modifications in the graph configuration of the proposed framework enable the use of partially annotated atlas images and investigate different partial annotation strategies. The proposed method was evaluated on two Magnetic Resonance Imaging (MRI) datasets for hippocampal and cardiac segmentation. Experiments were performed aimed at (1) recreating existing segmentation techniques with the proposed framework and (2) demonstrating the potential of employing sparsely annotated atlas data for multi-atlas segmentation.
Issue Date: 1-Jul-2018
Date of Acceptance: 22-Aug-2017
URI: http://hdl.handle.net/10044/1/52443
DOI: https://dx.doi.org/10.1109/TPAMI.2017.2711020
ISSN: 0162-8828
Publisher: Institute of Electrical and Electronics Engineers
Start Page: 1683
End Page: 1696
Journal / Book Title: IEEE Transactions on Pattern Analysis and Machine Intelligence
Volume: 40
Issue: 7
Copyright Statement: © 2017, 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: Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Funder's Grant Number: EP/I000445/1
FP7 - 601055
Keywords: cs.CV
0801 Artificial Intelligence And Image Processing
0806 Information Systems
0906 Electrical And Electronic Engineering
Artificial Intelligence & Image Processing
Publication Status: Published
Online Publication Date: 2017-08-22
Appears in Collections:Faculty of Engineering
Computing
Department of Medicine
Faculty of Medicine



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