基于稠密块和注意力机制的肺部病理图像异常细胞分割

doi:10.11959/j.issn.2096-6652.202210

Abstract

Abstract:

Aiming at the problems of unbalanced brightness of lung cell images and achieving accurate segmentation of abnormal cell contour difficultly, an abnormal cell segmentation model based on U-Net was proposed, which combined the dense connection mechanism and attention mechanism. Firstly, U-Net with encoder-decoder structure was used to segment abnormal cells. Secondly, the dense block was introduced into U-Net to improve the propagation ability between features and extract more characteristic information of abnormal cells. Finally, the attention mechanism was used to increase the weight of abnormal cell regions and reduce the interference of the imbalance of brightness to the model. The experimental results show that the IoU value and Dice similarity coefficient achieved by this method are 0.6928 and 0.8060, respectively. Compared with other models, this proposed method is able to segment low-contrast regions and abnormal cells with diverse shapes.

Key words: lung cell pathology image, cell segmentation, U-Net, dense block, attention mechanism

CLC Number:

TP391

Wencheng CUI,Keli WANG,Hong SHAO. Abnormal cell segmentation for lung pathological image based on denseblock and attention mechanism[J]. Chinese Journal of Intelligent Science and Technology, 2023, 5(4): 525-534.

Figures/Tables 15

References 25

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模型	IoU	DSC
FCN-8s	0.6231	0.7480
U-Net	0.6519	0.7743
R2U-Net	0.6598	0.7762
本文模型	0.6928	0.8070

模型	IoU	DSC
U-Net	0.6519	0.7743
Dense-UNet	0.6624	0.7773
Attention-UNet	0.6748	0.7875
本文模型	0.6928	0.8070

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Abnormal cell segmentation for lung pathological image based on denseblock and attention mechanism

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