{"title":"DCA-U-Net:用于小鼠皮肤OCT图像激光热损伤区域分割的深度学习网络。","authors":"Chenliang Xu, Qiong Ma, Jingyuan Wu, Yu Wei, Qi Liu, Qingyu Cai, Haiyang Sun, Xiaoan Tang, Hongxiang Kang","doi":"10.1088/2057-1976/adcd7c","DOIUrl":null,"url":null,"abstract":"<p><p>Laser-induced thermal injury is a common form of skin damage in clinical treatment, and accurately assessing the extent of injury and treatment efficacy is crucial for patient recovery. In recent years, deep learning models have been increasingly applied to the automatic segmentation of skin injury regions. However, existing methods often suffer from a large number of parameters, leading to a significant decline in segmentation accuracy when reducing the number of model parameters, thus limiting their clinical applicability. To address this issue, we propose an efficient and lightweight segmentation model, Dilated ConvNeXT Attention U-Net (DCA-U-Net), based on U-Net. By incorporating the more efficient Dilated ConvNeXT Block (DCB) and Dual Module Attention Block (DMAB), DCA-U-Net significantly reduces the number of parameters while simultaneously improving feature extraction capability and segmentation accuracy. Compared to the standard U-Net, our model reduces the number of parameters by 33%. Experimental results on two different sections of mouse skin laser thermal damage Optical Coherence Tomography (OCT) datasets show that our model has better segmentation performance with insufficient or sufficient amount of data. These improvements not only enhance the model's ability to accurately identify skin thermal injury regions, but also substantially reduce computational costs while maintaining high segmentation accuracy, offering promising technical support for the precise diagnosis and treatment of skin laser thermal injuries.</p>","PeriodicalId":8896,"journal":{"name":"Biomedical Physics & Engineering Express","volume":" ","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"DCA-U-Net: a deep learning network for segmentation of laser-induced thermal damage regions in mouse skin OCT images.\",\"authors\":\"Chenliang Xu, Qiong Ma, Jingyuan Wu, Yu Wei, Qi Liu, Qingyu Cai, Haiyang Sun, Xiaoan Tang, Hongxiang Kang\",\"doi\":\"10.1088/2057-1976/adcd7c\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Laser-induced thermal injury is a common form of skin damage in clinical treatment, and accurately assessing the extent of injury and treatment efficacy is crucial for patient recovery. In recent years, deep learning models have been increasingly applied to the automatic segmentation of skin injury regions. However, existing methods often suffer from a large number of parameters, leading to a significant decline in segmentation accuracy when reducing the number of model parameters, thus limiting their clinical applicability. To address this issue, we propose an efficient and lightweight segmentation model, Dilated ConvNeXT Attention U-Net (DCA-U-Net), based on U-Net. By incorporating the more efficient Dilated ConvNeXT Block (DCB) and Dual Module Attention Block (DMAB), DCA-U-Net significantly reduces the number of parameters while simultaneously improving feature extraction capability and segmentation accuracy. Compared to the standard U-Net, our model reduces the number of parameters by 33%. Experimental results on two different sections of mouse skin laser thermal damage Optical Coherence Tomography (OCT) datasets show that our model has better segmentation performance with insufficient or sufficient amount of data. These improvements not only enhance the model's ability to accurately identify skin thermal injury regions, but also substantially reduce computational costs while maintaining high segmentation accuracy, offering promising technical support for the precise diagnosis and treatment of skin laser thermal injuries.</p>\",\"PeriodicalId\":8896,\"journal\":{\"name\":\"Biomedical Physics & Engineering Express\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomedical Physics & Engineering Express\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1088/2057-1976/adcd7c\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomedical Physics & Engineering Express","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1088/2057-1976/adcd7c","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
DCA-U-Net: a deep learning network for segmentation of laser-induced thermal damage regions in mouse skin OCT images.
Laser-induced thermal injury is a common form of skin damage in clinical treatment, and accurately assessing the extent of injury and treatment efficacy is crucial for patient recovery. In recent years, deep learning models have been increasingly applied to the automatic segmentation of skin injury regions. However, existing methods often suffer from a large number of parameters, leading to a significant decline in segmentation accuracy when reducing the number of model parameters, thus limiting their clinical applicability. To address this issue, we propose an efficient and lightweight segmentation model, Dilated ConvNeXT Attention U-Net (DCA-U-Net), based on U-Net. By incorporating the more efficient Dilated ConvNeXT Block (DCB) and Dual Module Attention Block (DMAB), DCA-U-Net significantly reduces the number of parameters while simultaneously improving feature extraction capability and segmentation accuracy. Compared to the standard U-Net, our model reduces the number of parameters by 33%. Experimental results on two different sections of mouse skin laser thermal damage Optical Coherence Tomography (OCT) datasets show that our model has better segmentation performance with insufficient or sufficient amount of data. These improvements not only enhance the model's ability to accurately identify skin thermal injury regions, but also substantially reduce computational costs while maintaining high segmentation accuracy, offering promising technical support for the precise diagnosis and treatment of skin laser thermal injuries.
期刊介绍:
BPEX is an inclusive, international, multidisciplinary journal devoted to publishing new research on any application of physics and/or engineering in medicine and/or biology. Characterized by a broad geographical coverage and a fast-track peer-review process, relevant topics include all aspects of biophysics, medical physics and biomedical engineering. Papers that are almost entirely clinical or biological in their focus are not suitable. The journal has an emphasis on publishing interdisciplinary work and bringing research fields together, encompassing experimental, theoretical and computational work.