A Medical Image Segmentation Network with Boundary Enhancement

SUN Junmei; GE Qingqing; LI Xiumei; ZHAO Baoqi

doi:10.11999/JEIT210784

Volume 44 Issue 5

May 2022

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Article Navigation > Journal of Electronics & Information Technology > 2022 > 44(5): 1643-1652

SUN Junmei, GE Qingqing, LI Xiumei, ZHAO Baoqi. A Medical Image Segmentation Network with Boundary Enhancement[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1643-1652. doi: 10.11999/JEIT210784

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SUN Junmei, GE Qingqing, LI Xiumei, ZHAO Baoqi. A Medical Image Segmentation Network with Boundary Enhancement[J]. Journal of Electronics & Information Technology, 2022, 44(5): 1643-1652. doi: 10.11999/JEIT210784

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A Medical Image Segmentation Network with Boundary Enhancement

doi: 10.11999/JEIT210784

School of Information Science and Technology, Hangzhou Normal University, Hangzhou 311121, China

Funds: The National Natural Science Foundation of China (61801159, 61571174), The Open Fund of Engineering Research Center for Software Testing and Evaluation of Fujian Province (ST2019004), The Science and Technology Plan Project of Hangzhou (20201203B124)

Received Date: 2021-08-06
Accepted Date: 2021-12-14
Rev Recd Date: 2021-12-10

Available Online: 2021-12-26

Publish Date: 2022-05-25

Abstract

Abstract

A medical image segmentation network with boundary enhancement, named as the AS-UNet (Add-and-Subtract UNet), is proposed to solve the problems of traditional segmentation networks for medical images, such as unclear boundary segmentation and large missing value. The mask boundary image is obtained by using the mask boundary image extraction algorithm, and the Boundary Attention Block (BAB) with multi-scale feature maps is introduced into the last three layers of the UNet expansion path. Moreover, the combinatorial loss function is proposed to improve the segmentation accuracy. In testing, the BAB can be abandoned to reduce testing parameters. Comparisons with other segmentation methods on three different types of medical image segmentation datasets, Glas, DRIVE and ISIC2018 are provided, indicating that the segmentation performance of the AS-UNet is better.
- Medical image segmentation,
- Attention mechanism,
- Boundary attention,
- Combinatorial loss function

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References(30)

References

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