DSU-Net:基于 CNN 和变换器的双级 U-Net 用于皮损分割

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Longwei Zhong , Tiansong Li , Meng Cui , Shaoguo Cui , Hongkui Wang , Li Yu
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引用次数: 0

摘要

从皮肤镜图片中精确划分皮肤病变对于加强黑色素瘤的定量分析至关重要。然而,由于皮损的大小、形态和模糊边界等固有特征存在较大差异,这仍然是一项艰巨的任务。近年来,CNN 和变换器在皮损分割领域取得了显著成效。因此,我们首先提出了 DSU-Net 分割网络,其灵感来源于人工分割过程。通过两个分割子网络的协调机制,模拟出一个皮损区域从粗略识别到细致划分的过程。然后,我们提出了一个两阶段平衡损失函数,通过自适应控制损失权重来更好地模拟人工分割过程。此外,我们还引入了多特征融合模块,结合各种特征提取模块,提取更丰富的特征信息,细化病变区域,获得准确的分割边界。最后,我们在 ISIC2017、ISIC2018 和 PH2 数据集上进行了大量实验,通过与目前最先进的方法进行比较,评估和验证了 DSU-Net 的功效。代码见 https://github.com/ZhongLongwei/DSU-Net。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
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.
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