Longwei Zhong , Tiansong Li , Meng Cui , Shaoguo Cui , Hongkui Wang , Li Yu
{"title":"DSU-Net:基于 CNN 和变换器的双级 U-Net 用于皮损分割","authors":"Longwei Zhong , Tiansong Li , Meng Cui , Shaoguo Cui , Hongkui Wang , Li Yu","doi":"10.1016/j.bspc.2024.107090","DOIUrl":null,"url":null,"abstract":"<div><div>Precise delineation of skin lesions from dermoscopy pictures is crucial for enhancing the quantitative analysis of melanoma. However, this remains a difficult endeavor due to inherent characteristics such as large variability in lesion size, form, and fuzzy boundaries. In recent years, CNNs and Transformers have indicated notable benefits in the area of skin lesion segmentation. Hence, we first propose the DSU-Net segmentation network, which is inspired by the manual segmentation process. Through the coordination mechanism of the two segmentation sub-networks, the simulation of a process occurs where the lesion area is initially coarsely identified and then meticulously delineated. Then, we propose a two-stage balanced loss function to better simulate the manual segmentation process by adaptively controlling the loss weight. Further, we introduce a multi-feature fusion module, which combines various feature extraction modules to extract richer feature information, refine the lesion area, and obtain accurate segmentation boundaries. Finally, we conducted extensive experiments on the ISIC2017, ISIC2018, and PH2 datasets to assess and validate the efficacy of the DSU-Net by comparing it to the most advanced approaches currently available. The codes are available at <span><span>https://github.com/ZhongLongwei/DSU-Net</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55362,"journal":{"name":"Biomedical Signal Processing and Control","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DSU-Net: Dual-Stage U-Net based on CNN and Transformer for skin lesion segmentation\",\"authors\":\"Longwei Zhong , Tiansong Li , Meng Cui , Shaoguo Cui , Hongkui Wang , Li Yu\",\"doi\":\"10.1016/j.bspc.2024.107090\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Precise delineation of skin lesions from dermoscopy pictures is crucial for enhancing the quantitative analysis of melanoma. However, this remains a difficult endeavor due to inherent characteristics such as large variability in lesion size, form, and fuzzy boundaries. In recent years, CNNs and Transformers have indicated notable benefits in the area of skin lesion segmentation. Hence, we first propose the DSU-Net segmentation network, which is inspired by the manual segmentation process. Through the coordination mechanism of the two segmentation sub-networks, the simulation of a process occurs where the lesion area is initially coarsely identified and then meticulously delineated. Then, we propose a two-stage balanced loss function to better simulate the manual segmentation process by adaptively controlling the loss weight. Further, we introduce a multi-feature fusion module, which combines various feature extraction modules to extract richer feature information, refine the lesion area, and obtain accurate segmentation boundaries. Finally, we conducted extensive experiments on the ISIC2017, ISIC2018, and PH2 datasets to assess and validate the efficacy of the DSU-Net by comparing it to the most advanced approaches currently available. The codes are available at <span><span>https://github.com/ZhongLongwei/DSU-Net</span><svg><path></path></svg></span>.</div></div>\",\"PeriodicalId\":55362,\"journal\":{\"name\":\"Biomedical Signal Processing and Control\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Signal Processing and Control\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1746809424011480\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Signal Processing and Control","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1746809424011480","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
DSU-Net: Dual-Stage U-Net based on CNN and Transformer for skin lesion segmentation
Precise delineation of skin lesions from dermoscopy pictures is crucial for enhancing the quantitative analysis of melanoma. However, this remains a difficult endeavor due to inherent characteristics such as large variability in lesion size, form, and fuzzy boundaries. In recent years, CNNs and Transformers have indicated notable benefits in the area of skin lesion segmentation. Hence, we first propose the DSU-Net segmentation network, which is inspired by the manual segmentation process. Through the coordination mechanism of the two segmentation sub-networks, the simulation of a process occurs where the lesion area is initially coarsely identified and then meticulously delineated. Then, we propose a two-stage balanced loss function to better simulate the manual segmentation process by adaptively controlling the loss weight. Further, we introduce a multi-feature fusion module, which combines various feature extraction modules to extract richer feature information, refine the lesion area, and obtain accurate segmentation boundaries. Finally, we conducted extensive experiments on the ISIC2017, ISIC2018, and PH2 datasets to assess and validate the efficacy of the DSU-Net by comparing it to the most advanced approaches currently available. The codes are available at https://github.com/ZhongLongwei/DSU-Net.
期刊介绍:
Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management.
Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.