U-net-based enhanced COVID-19 lung automatic segmentation model for infected regions of CT images

Guoliang Wang; Chaoyang Li

doi:10.1117/12.3037344

22 October 2024 U-net-based enhanced COVID-19 lung automatic segmentation model for infected regions of CT images

Guoliang Wang, Chaoyang Li

Author Affiliations +

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132741H (2024) https://doi.org/10.1117/12.3037344
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

COVID-19 outbreak has so far caused us much inconvenience, resulting in significant economic losses and casualties. CT scanning, as a fast and sensitive detection tool, plays an essential role in delineating infected regions. However, CT images of COVID-19 exhibit complex variations in infected regions and have limited expert annotation data. In this research, a novel Dilated Convolution (DC) module, incorporating a residual structure, is introduced to systematically broaden the sensory field of the model. Following this, the Pyramid Pooling Module (PPM) and Multiscale Attention Module (MSA) are synergistically fused to create an advanced multilayer attention module. Finally, the proposed Enhanced Decoder Path (EDPath) module serves the crucial role of bridging the gap between the encoder and decoder. This module effectively resolves challenges associated with the loss of high-resolution information and the issue of gradient vanishing during decoder transmission. A large number of ablation experiments and comparative experiments have demonstrated that each of the proposed modules is effective in improving the model performance, and the model outperforms the more advanced models in terms of DICE, IOU and SEN. This experimental study demonstrates that an improved segmentation network model based on Unet and attention mechanisms has high accuracy in the COVID-19 lesion segmentation task.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Guoliang Wang and Chaoyang Li "U-net-based enhanced COVID-19 lung automatic segmentation model for infected regions of CT images", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132741H (22 October 2024); https://doi.org/10.1117/12.3037344

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KEYWORDS

Image segmentation

COVID 19

Data modeling

Computed tomography

Performance modeling

Education and training

Lung

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