DRINet for medical image segmentation
File(s)
Accepted version
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2018-05-10
Date Acceptance
2018-05-03
Citation
IEEE Transactions on Medical Imaging, 2018, 37 (11), pp.2453-2462
ISSN
0278-0062
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2453
End Page
2462
Journal / Book Title
IEEE Transactions on Medical Imaging
Volume
37
Issue
11
Copyright Statement
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor
National Institute for Health Research
Grant Number
ll-LA-0814-20007
Subjects
Science & Technology
Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Engineering, Biomedical
Engineering, Electrical & Electronic
Imaging Science & Photographic Technology
Radiology, Nuclear Medicine & Medical Imaging
Computer Science
Engineering
Convolutional neural network
medical image segmentation
brain atrophy
abdominal organ segmentation
AUTOMATIC SEGMENTATION
CEREBROSPINAL-FLUID
MULTIORGAN SEGMENTATION
PROBABILISTIC ATLAS
ISCHEMIC-STROKE
Nuclear Medicine & Medical Imaging
08 Information and Computing Sciences
09 Engineering
Publication Status
Published
Date Publish Online
2018-05-10