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:2018:10.1109/TMI.2018.2835303,
author = {Chen, L and Bentley, P and Mori, K and Misawa, K and Fujiwara, M and Rueckert, D},
doi = {10.1109/TMI.2018.2835303},
journal = {IEEE Transactions on Medical Imaging},
pages = {2453--2462},
title = {DRINet for medical image segmentation},
url = {http://dx.doi.org/10.1109/TMI.2018.2835303},
volume = {37},
year = {2018}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Convolutional neural networks (CNNs) have revolutionized medical image analysis over the past few years. The UNet architecture is one of the most well-known CNN architectures for semantic segmentation and has achieved remarkable successes in many different medical image segmentation applications. The U-Net architecture consists of standard convolution layers, pooling layers, and upsampling layers. These convolution layers learn representative features of input images and construct segmentations based on the features. However, the features learned by standard convolution layers are not distinctive when the differences among different categories are subtle in terms of intensity, location, shape, and size. In this paper, we propose a novel CNN architecture, called Dense-Res-Inception Net (DRINet), which addresses this challenging problem. The proposed DRINet consists of three blocks, namely a convolutional block with dense connections, a deconvolutional block with residual Inception modules, and an unpooling block. Our proposed architecture outperforms the U-Net in three different challenging applications, namely multi-class segmentation of cerebrospinal fluid (CSF) on brain CT images, multi-organ segmentation on abdominal CT images, multi-class brain tumour segmentation on MR images.
AU  - Chen,L
AU  - Bentley,P
AU  - Mori,K
AU  - Misawa,K
AU  - Fujiwara,M
AU  - Rueckert,D
DO  - 10.1109/TMI.2018.2835303
EP  - 2462
PY  - 2018///
SN  - 0278-0062
SP  - 2453
TI  - DRINet for medical image segmentation
T2  - IEEE Transactions on Medical Imaging
UR  - http://dx.doi.org/10.1109/TMI.2018.2835303
UR  - http://hdl.handle.net/10044/1/59785
VL  - 37
ER  -
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