基于不确定性引导和尺度一致性的肾肿瘤图像分割方法

doi:10.16451/j.cnki.issn1003-6059.202302001

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Abstract Aiming at the problems of boundary blurring and feature loss in kidney tumor image segmentation, a kidney tumor image segmentation method based on uncertainty guidance and scale consistency is proposed on the basis of residual attention U-net model. For blurred boundaries of kidney tumor images, an uncertainty guidance module is introduced into the decoding layer to allocate weights adaptively based on uncertainty. Thus, the effect of wrong pixels is reduced and the boundary localization ability of the model is improved. For the problem of feature loss caused by down-sampling, the scale attention module and feature consistency loss are proposed. The multi-scale fusion strategy is utilized to fuse features of different scales, and the scale consistency constraint is conducted with the features at the bottom of the encoder to alleviate the problem of feature loss. Finally, experiments of kidney and kidney tumor segmentation on the public dataset KiTS19 demonstrate that the proposed segmentation method greatly improves the segmentation accuracy. In addition, the segmentation results of the proposed method hold better reliability due to the uncertainty guidance module.

Key words： Image Segmentation Uncertainty Guidance Scale Consistency Attention Module Kidney Tumor

Received: 11 November 2022

ZTFLH:

TP391.41

Fund:National Natural Science Foundation of China(No.62261039)

Corresponding Authors: ZHANG Guimei, Ph.D., professor. Her research interests include computer vision, image processing and pattern recognition.

About author:: HOU Bingzhen, master student. His research interests include computer vision, image processing and pattern recognition.PENG Kun, master student. His research interests include computer vision, image processing and pattern recognition.

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Cite this article:

HOU Bingzhen,ZHANG Guimei,PENG Kun. Kidney Tumor Image Segmentation Method Based on Uncertainty Guidance and Scale Consistency[J]. Pattern Recognition and Artificial Intelligence, 2023, 36(2): 95-107.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202302001 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2023/V36/I2/95

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