Imperial College London
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Dr Ben Glocker

Faculty of Engineering, Department of Computing

Professor in Machine Learning for Imaging
 
 
 
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Contact

 

+44 (0)20 7594 8334b.glocker Website CV

 
 
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Location

 

377Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Lavdas:2017:10.1002/mp.12492,
author = {Lavdas, I and Glocker, B and Kamnitsas, K and Rueckert, D and Mair, H and Sandhu, A and Taylor, SA and Aboagye, EO and Rockall, AG},
doi = {10.1002/mp.12492},
journal = {Medical Physics},
pages = {5210--5220},
title = {Fully automatic, multi-organ segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs) and a multi-atlas (MA) approach.},
url = {http://dx.doi.org/10.1002/mp.12492},
volume = {44},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - PURPOSE: As part of a programme to implement automatic lesion detection methods for whole body magnetic resonance imaging (MRI) in oncology, we have developed, evaluated and compared three algorithms for fully automatic, multi-organ segmentation in healthy volunteers. METHODS: The first algorithm is based on classification forests (CFs), the second is based on 3D convolutional neural networks (CNNs) and the third algorithm is based on a multi-atlas (MA) approach. We examined data from 51 healthy volunteers, scanned prospectively with a standardised, multi-parametric whole body MRI protocol at 1.5T. The study was approved by the local ethics committee and written consent was obtained from the participants. MRI data were used as input data to the algorithms, while training was based on manual annotation of the anatomies of interest by clinical MRI experts. Five-fold cross-validation experiments were run on 34 artefact-free subjects. We report three overlap and three surface distance metrics to evaluate the agreement between the automatic and manual segmentations, namely the Dice similarity coefficient (DSC), recall (RE), precision (PR), average surface distance (ASD), root mean square surface distance (RMSSD) and Hausdorff distance (HD). Analysis of variances was used to compare pooled label metrics between the three algorithms and the DSC on a 'per-organ' basis. A Mann-Whitney U test was used to compare the pooled metrics between CFs and CNNs and the DSC on a 'per-organ' basis, when using different imaging combinations as input for training. RESULTS: All three algorithms resulted in robust segmenters that were effectively trained using a relatively small number of data sets, an important consideration in the clinical setting. Mean overlap metrics for all the segmented structures were: CFs: DSC=0.70±0.18, RE=0.73±0.18, PR=0.71±0.14, CNNs: DSC=0.81±0.13, RE=0.83±0.14, PR=0.82±0.10, MA: DSC=0.71±0.22, RE=0.70±0.34
AU  - Lavdas,I
AU  - Glocker,B
AU  - Kamnitsas,K
AU  - Rueckert,D
AU  - Mair,H
AU  - Sandhu,A
AU  - Taylor,SA
AU  - Aboagye,EO
AU  - Rockall,AG
DO  - 10.1002/mp.12492
EP  - 5220
PY  - 2017///
SN  - 0094-2405
SP  - 5210
TI  - Fully automatic, multi-organ segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs) and a multi-atlas (MA) approach.
T2  - Medical Physics
UR  - http://dx.doi.org/10.1002/mp.12492
UR  - http://hdl.handle.net/10044/1/50095
VL  - 44
ER  -
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