research-article

AMF-UNet: A CT Image Segmentation Approach of Pulmonary Tuberculosis

Authors:

Kangjian HeAuthors Info & Claims

ICIGP '21: Proceedings of the 2021 4th International Conference on Image and Graphics Processing

Pages 182 - 188

https://doi.org/10.1145/3447587.3447614

Published: 04 June 2021 Publication History

Abstract

Tuberculosis disease is a serious threat to human life and health, and CT images of Tuberculosis shows a variety of pathological manifestations causing damage to the lungs. To solve the problem that traditional segmentation methods for CT images cannot accurately segment damaged lungs, we proposed an approach, Attention Multi-scale Fusion UNet (AMF-UNet), for tuberculosis lung CT image segmentation based on convolutional neural network. In this work, we designed a dual attention mechanism in the model to retain important spatial information of damaged lungs and improve segmentation accuracy. At the same time, in order to extract deeper semantic information, the atrous spatial convolution pooling pyramid was introduced. In particular, we designed a multi-scale fusion module in the decoding structure for the first time to reduce the information loss of damaged lungs during the up-sampling process. Compared with several enhancement methods, the proposed model achieves better subjective results on the ImageClef19 tuberculosis CT dataset.

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Cited By

Sineglazov VRiazanovskiy KKlanovets AChumachenko ELinnik N(2022)Intelligent tuberculosis activity assessment system based on an ensemble of neural networksComputers in Biology and Medicine10.1016/j.compbiomed.2022.105800147:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.compbiomed.2022.105800

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ICIGP '21: Proceedings of the 2021 4th International Conference on Image and Graphics Processing

January 2021

231 pages

ISBN:9781450389105

DOI:10.1145/3447587

Copyright © 2021 ACM.

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Association for Computing Machinery

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Publication History

Published: 04 June 2021

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The Yunnan Province Ten Thousand Talents Program and Yun-Ling Scholars Special Project
The Yunnan Provincial Science and Technology Department-Yunnan University Double First Class Construction Joint Fund Project
The National Natural Science Foundation of China

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ICIGP 2021

ICIGP 2021: 2021 The 4th International Conference on Image and Graphics Processing

January 1 - 3, 2021

Sanya, China

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Cited By

Sineglazov VRiazanovskiy KKlanovets AChumachenko ELinnik N(2022)Intelligent tuberculosis activity assessment system based on an ensemble of neural networksComputers in Biology and Medicine10.1016/j.compbiomed.2022.105800147:COnline publication date: 1-Aug-2022
https://dl.acm.org/doi/10.1016/j.compbiomed.2022.105800

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