Imperial College London
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DrPaulBentley

Faculty of Medicine, Department of Brain Sciences

Senior Clinical Research Fellow
 
 
 
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Contact

 

p.bentley

 
 
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Location

 

10L21Charing Cross HospitalCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Chen:2017:10.1016/j.nicl.2017.06.016,
author = {Chen, L and Bentley, P and Rueckert, D},
doi = {10.1016/j.nicl.2017.06.016},
journal = {NeuroImage: Clinical},
pages = {633--643},
title = {Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks},
url = {http://dx.doi.org/10.1016/j.nicl.2017.06.016},
volume = {15},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifyingthem manually is costly and challenging for clinicians. In this paper, we propose a novel framework to auto-matically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs):one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.
AU  - Chen,L
AU  - Bentley,P
AU  - Rueckert,D
DO  - 10.1016/j.nicl.2017.06.016
EP  - 643
PY  - 2017///
SN  - 2213-1582
SP  - 633
TI  - Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks
T2  - NeuroImage: Clinical
UR  - http://dx.doi.org/10.1016/j.nicl.2017.06.016
UR  - http://hdl.handle.net/10044/1/49066
VL  - 15
ER  -
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