Endometrial cancer lesion region segmentation based on large kernel convolution and combined attention.
10.7507/1001-5515.202502023
- Author:
Rushu PENG
1
;
Qinghao ZENG
1
;
Bin HE
1
;
Junjie LIU
1
;
Zhang XIAO
1
Author Information
1. School of Mechanical Engineering, University of South China, Hengyang, Hunan 421200, P. R. China.
- Publication Type:Journal Article
- Keywords:
Computed tomography;
Deep learning;
Endometrial cancer;
Image segmentation
- MeSH:
Humans;
Endometrial Neoplasms/diagnostic imaging*;
Female;
Tomography, X-Ray Computed/methods*;
Deep Learning;
Algorithms;
Image Processing, Computer-Assisted/methods*;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2025;42(5):928-935
- CountryChina
- Language:Chinese
-
Abstract:
Endometrial cancer (EC) is one of the most common gynecological malignancies, with an increasing incidence rate worldwide. Accurate segmentation of lesion areas in computed tomography (CT) images is a critical step in assisting clinical diagnosis. In this study, we propose a novel deep learning-based segmentation model, termed spatial choice and weight union network (SCWU-Net), which incorporates two newly designed modules: the spatial selection module (SSM) and the combination weight module (CWM). The SSM enhances the model's ability to capture contextual information through deep convolutional blocks, while the CWM, based on joint attention mechanisms, is employed within the skip connections to further boost segmentation performance. By integrating the strengths of both modules into a U-shaped multi-scale architecture, the model achieves precise segmentation of EC lesion regions. Experimental results on a public dataset demonstrate that SCWU-Net achieves a Dice similarity coefficient (DSC) of 82.98%, an intersection over union (IoU) of 78.63%, a precision of 92.36%, and a recall of 84.10%. Its overall performance is significantly outperforming other state-of-the-art models. This study enhances the accuracy of lesion segmentation in EC CT images and holds potential clinical value for the auxiliary diagnosis of endometrial cancer.
