Segmentation of lung tumors based on PCU-Net

CAI Hao; LI Peng; GONG Xiaomei; WANG Raofen

doi:10.12299/jsues.24-0012

Volume 38 Issue 4

Dec. 2024

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Article Navigation > Journal of Shanghai University of Engineering Science > 2024 > 38(4): 444-450

CAI Hao, LI Peng, GONG Xiaomei, WANG Raofen. Segmentation of lung tumors based on PCU-Net[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 444-450. doi: 10.12299/jsues.24-0012

Citation:

CAI Hao, LI Peng, GONG Xiaomei, WANG Raofen. Segmentation of lung tumors based on PCU-Net[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 444-450. doi: 10.12299/jsues.24-0012

CAI Hao, LI Peng, GONG Xiaomei, WANG Raofen. Segmentation of lung tumors based on PCU-Net[J]. Journal of Shanghai University of Engineering Science, 2024, 38(4): 444-450. doi: 10.12299/jsues.24-0012

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Segmentation of lung tumors based on PCU-Net

doi: 10.12299/jsues.24-0012

CAI Hao^1
,,
LI Peng²,
GONG Xiaomei²,
WANG Raofen^{1
,
,}

1.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai 201620, China
2.
Department of Radiation Oncology, Shanghai Pulmonary Hospital, Shanghai 200433, China

Received Date: 2024-01-12
Publish Date: 2024-12-31

Abstract

Abstract

Deep learning techniques can assist doctors in precise tumor segmentation. However, existing methods often suffer from issues such as fuzzy segmentation edges and large model parameter counts due to the unclear boundaries between lung tumors and surrounding tissues. A partial convolution coordinate attention U-net (PCU-Net) algorithm for lightweight lung tumor segmentation was proposed. The partial convolution was introduced to reduce model parameters and enhance feature extraction capability. The coordinate attention module was added at skip connection of PCU-Net, so that more precise localization of tumors was achieved by network and segmentation accuracy was improved. The research result shows that the improved PCU-Net can reduce model parameters by 58.57% while increase Dice coefficient, Intersection over Union (IoU) and Recall by 4.22%, 4.26% and 6.82%, respectively. The comparison between PPU-Net and other semantic segmentation models shows that Dice coefficient of PCU-Net is 3-6 percentage points higher than that of other models.
- segmentation of lung tumors,
- partial convolution,
- coordinate attention mechanism

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References(15)

References

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