Lightweight U-Net model for segmentation of breast cancer ultrasound images
10.13929/j.issn.1672-8475.2024.10.011
- VernacularTitle:基于轻量化U-Net模型分割乳腺癌超声图像
- Author:
Ruizhi ZHANG
1
;
Chong YANG
;
Dong XU
Author Information
1. 浙江中医药大学研究生院,浙江 杭州 310053;舟山市普陀区人民医院超声科,浙江 舟山 316100
- Keywords:
breast neoplasms;
ultrasonography;
neural networks,computer;
artificial intelligence
- From:
Chinese Journal of Interventional Imaging and Therapy
2024;21(10):618-623
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the value of lightweight U-Net(L-U-Net)model for segmentation of breast cancer ultrasound images.Methods A total of 1 009 ultrasound images of 779 female cases with breast cancer were retrospectively analyzed,including 807 images in training set and 202 images in test set.MobileNetV2 and MobileViT modules were embedded into encoding end of U-Net model to construct L-U-Net models,i.e.conventional lightweight L-U-Net(L-U-Net 1)and sub lightweight L-U-Net(L-U-Net 2)models.The segmentation accuracy and lightweight degree of L-U-Net models were evaluated taken manually annotating lesion areas by physicians as the reference standards.Results The pixel accuracy,intersection over union and Dice similarity coefficient of L-U-Net models for segmentation of breast cancer ultrasound images were similar to those of U-Net model,and the number of parameters,floating point operation and memory usage of L-U-Net model were lower but inference time were higher than those of U-Net model.U-Net and L-U-Net models had better segmentation efficacy for ultrasound images of breast cancer with clear boundaries.For images with blurred lesion boundaries but still recognizable,U-Net model was prone to mislabeling non lesion areas,while L-U-Net models could provide more accurate segmentation results.For images with blurred lesion boundaries difficult to identify with naked eyes,all 3 models had incomplete segmentation,among which U-Net and L-U-Net 1 models had larger missing areas but L-U-Net 2 model had smaller missing areas.Conclusion L-U-Net 2 model could be used for segmentation of breast cancer ultrasound images with good lightweight degree and segmentation accuracy.
