NDLI: Medical image segmentation using deep learning with feature enhancement

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Medical image segmentation using deep learning with feature enhancement

Content Provider	IET Digital Library
Author	Huang, Shaoqiong Huang, Mengxing Zhang, Yu Chen, Jing Bhatti, Uzair
Abstract	Pre-segmentation is known as a crucial step in medical image analysis. Many approaches have been proposed to make improvement to both the quality and efficiency of segmentation. However, existing methods are lacking in robustness to the variation in the edges and textures of the target. In order to address these drawbacks, a novel attention Gabor network (AGnet) based on deep learning for medical image segmentation that is capable of automatically paying more attention to the edge and consistently for improvement to the segmentation performance is proposed. The proposed model consists of two components. The first one is to determine the approximate location of the organs of interest in the image using convolution filters, and the other one is to highlight salient edge features intended for a specific segmentation task using Gabor filters. In order to facilitate collaboration in between the two parts, a region attention mechanism based on Gabor maps is suggested. The mechanism improved performance by learning to focus on the salient regions of the image that are useful for the authors' tasks. As indicated by the experimental results, the AGnet is capable of enhancing the prediction performance while maintaining the computational efficiency, which makes it comparable with other state-of-the-art approaches.
Starting Page	3324
Ending Page	3332
Page Count	9
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0772
Journal	IET Image Processing
Publisher Date	2020-02-21
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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