SET: Superpixel Embedded Transformer for skin lesion segmentation

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhonghua Wang , Junyan Lyu , Xiaoying Tang
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引用次数: 0

Abstract

Accurate skin lesion segmentation is crucial for the early detection and treatment of skin cancer. Despite significant advances in deep learning, current segmentation methods often struggle to fully capture global contextual information and maintain the structural integrity of skin lesions. To address these challenges, this paper introduces Superpixel Embedded Transformer (SET), which integrates superpixels into the Transformer framework for skin lesion segmentation. Instead of embedding non-overlapping patches as tokens, SET employs an Association Embedded Merging & Dispatching (AEM&D) module to treat superpixels as the fundamental units during both the down-sampling and up-sampling phases. To better capture the multi-scale information of lesions, we propose a superpixel bank to store various superpixel maps with distinct compactness values. An Ensemble Fusion and Refinery (EFR) module is then designed to fuse and refine the results obtained from each map in the superpixel bank. This approach enables the model to selectively focus on different features by adopting various superpixel maps, thereby enhancing the segmentation performance. Extensive experiments are conducted on multiple skin lesion segmentation datasets, including ISIC 2016, ISIC 2017, and ISIC 2018. Comparative analyses with state-of-the-art methods showcase SET’s superior performance, and ablation studies confirm the effectiveness of our proposed modules incorporating superpixels into Vision Transformer. The source code of our SET will be available at https://github.com/Wzhjerry/SET.
SET:用于皮肤病变分割的超像素嵌入式变压器
准确的皮肤病灶分割对于皮肤癌的早期发现和治疗至关重要。尽管深度学习取得了重大进展,但目前的分割方法往往难以完全捕获全局上下文信息并保持皮肤病变的结构完整性。为了解决这些问题,本文引入了超像素嵌入式变压器(SET),它将超像素集成到Transformer框架中用于皮肤病变分割。SET没有将不重叠的补丁作为令牌嵌入,而是采用了关联嵌入合并调度(aem&d)模块在下采样和上采样阶段都将超像素作为基本单元。为了更好地捕获病变的多尺度信息,我们提出了一个超像素库来存储具有不同紧凑值的各种超像素图。然后设计了一个集成融合和精炼(EFR)模块来融合和精炼从超像素库中的每个地图中获得的结果。该方法通过采用不同的超像素图,使模型能够选择性地关注不同的特征,从而提高分割性能。在ISIC 2016、ISIC 2017和ISIC 2018等多个皮肤病变分割数据集上进行了大量实验。与最先进的方法比较分析显示了SET的卓越性能,烧蚀研究证实了我们提出的将超像素集成到Vision Transformer中的模块的有效性。我们的SET的源代码可以在https://github.com/Wzhjerry/SET上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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