Imperial College London
Alternate

ProfessorDanielRueckert

Faculty of Engineering, Department of Computing

Professor of Visual Information Processing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Koch:2018:10.1109/TPAMI.2017.2711020,
author = {Koch, LM and Rajchl, M and Bai, W and Baumgartner, CF and Tong, T and Passerat-Palmbach, J and Aljabar, P and Rueckert, D},
doi = {10.1109/TPAMI.2017.2711020},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
pages = {1683--1696},
title = {Multi-atlas segmentation using partially annotated data: methods and annotation strategies},
url = {http://dx.doi.org/10.1109/TPAMI.2017.2711020},
volume = {40},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Koch,LM
AU  - Rajchl,M
AU  - Bai,W
AU  - Baumgartner,CF
AU  - Tong,T
AU  - Passerat-Palmbach,J
AU  - Aljabar,P
AU  - Rueckert,D
DO  - 10.1109/TPAMI.2017.2711020
EP  - 1696
PY  - 2018///
SN  - 0162-8828
SP  - 1683
TI  - Multi-atlas segmentation using partially annotated data: methods and annotation strategies
T2  - IEEE Transactions on Pattern Analysis and Machine Intelligence
UR  - http://dx.doi.org/10.1109/TPAMI.2017.2711020
UR  - http://hdl.handle.net/10044/1/52443
VL  - 40
ER  -
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