Liver tumor image segmentation method based on cascaded DDR-UNet++
10.3969/j.issn.1005-202X.2025.07.009
- VernacularTitle:基于级联DDR-UNet++的肝脏肿瘤图像分割方法
- Author:
Yunkun HU
1
;
Xiaoyan WANG
1
;
Xiujuan WANG
1
Author Information
1. 山东第一医科大学(山东省医学科学院)医学信息与人工智能学院,山东 泰安 271000
- Publication Type:Journal Article
- Keywords:
DDR-UNet++;
U-Net;
residual module;
dilated convolution;
liver tumor segmentation
- From:
Chinese Journal of Medical Physics
2025;42(7):901-910
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore and address the issue of insufficient segmentation accuracy in liver tumor segmentation using the traditional U-Net algorithm,which is caused by the lack of contextual information for both the liver and tumor,as well as the large morphological variability of tumors.Methods A cascaded liver tumor segmentation algorithm,DDR-UNet++,which integrated dilated convolutions and residual modules was proposed.Firstly,CT images from the LiTS-2017 dataset were preprocessed through window width/level adjustment,histogram equalization and Gaussian filtering to reduce noise and smooth edges.Then,a cascaded liver segmentation model was employed to enhance the liver region proportion,mitigate interference from surrounding tissues and address data imbalance issue.For liver tumor segmentation,deformable dilated convolutions and residual networks were introduced to expand the receptive field and improve feature extraction capability.Results DDR-UNet++outperformed the traditional U-Net on the LiTS-2017 dataset,achieving improvements of 4.7%,1.7%,and 8.5%in Dice similarity coefficient,relative volume difference,and Jaccard index,respectively.These enhancements contribute to overcoming the inefficiency and low accuracy issues in conventional tumor segmentation,thereby improving early tumor detection rates,enhancing patient survival outcomes,and alleviating the diagnostic burden on clinicians.Conclusion The proposed method improves the feature extraction capability to some extent by enhancing the model structure and segmentation strategy,effectively increases the accuracy and robustness of liver tumor segmentation,and provides a reliable technical reference for clinical auxiliary diagnosis.
