research-article

A Segmentation Model of Lung Parenchyma in Chest CT Based on ResUnet

Authors:

Ying ChenAuthors Info & Claims

ICMLC '22: Proceedings of the 2022 14th International Conference on Machine Learning and Computing

Pages 429 - 434

https://doi.org/10.1145/3529836.3529917

Published: 21 June 2022 Publication History

Abstract

Segmentation of the lung parenchymal region in chest CT is an essential part of the automatic diagnosis of lung diseases. Therefore, the quality of the segmentation directly affects the results of the automatic diagnosis. This paper proposes a model for lung parenchymal segmentation in chest CT based on ResUnet. It introduces the residual learning unit to transfer low-level information and enhances the connection between layers using skip connections based on the U-Net architecture. Then, it achieves full feature extraction through down-convolution and up-sampling and uses image enhancement and data augmentation to preprocess the data set. Through experiment, the proposed segmentation model has better results than the IoU and Dice of other models and can better segment the lung parenchyma in chest CT.

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Cited By

Pawar LPatil SDhotre SSonawane S(2024)A Comparative Study of Artificial Intelligence and eXplainable AI Techniques for Pulmonary Disease Detection and Its Severity Classification2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)10.1109/IDAP64064.2024.10710903(1-7)Online publication date: 21-Sep-2024
https://doi.org/10.1109/IDAP64064.2024.10710903
Francis SJayaraj PPournami PPuzhakkal N(2023)ContourGAN: Auto‐contouring of organs at risk in abdomen computed tomography images using generative adversarial networkInternational Journal of Imaging Systems and Technology10.1002/ima.2290133:5(1494-1504)Online publication date: 26-Apr-2023
https://doi.org/10.1002/ima.22901

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ICMLC '22: Proceedings of the 2022 14th International Conference on Machine Learning and Computing

February 2022

570 pages

ISBN:9781450395700

DOI:10.1145/3529836

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 June 2022

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ICMLC 2022

ICMLC 2022: 2022 14th International Conference on Machine Learning and Computing

February 18 - 21, 2022

Guangzhou, China

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Cited By

Pawar LPatil SDhotre SSonawane S(2024)A Comparative Study of Artificial Intelligence and eXplainable AI Techniques for Pulmonary Disease Detection and Its Severity Classification2024 8th International Artificial Intelligence and Data Processing Symposium (IDAP)10.1109/IDAP64064.2024.10710903(1-7)Online publication date: 21-Sep-2024
https://doi.org/10.1109/IDAP64064.2024.10710903
Francis SJayaraj PPournami PPuzhakkal N(2023)ContourGAN: Auto‐contouring of organs at risk in abdomen computed tomography images using generative adversarial networkInternational Journal of Imaging Systems and Technology10.1002/ima.2290133:5(1494-1504)Online publication date: 26-Apr-2023
https://doi.org/10.1002/ima.22901

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