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首页> 外文期刊>Journal of medical systems >A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning
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A Computer-Aided Decision Support System for Detection and Localization of Cutaneous Vasculature in Dermoscopy Images Via Deep Feature Learning

机译：一种计算机辅助决策支持系统，通过深度特征学习检测和定位皮肤脉络膜脉冲脉管系统

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Vascular structures of skin are important biomarkers in diagnosis and assessment of cutaneous conditions. Presence and distribution of lesional vessels are associated with specific abnormalities. Therefore, detection and localization of cutaneous vessels provide critical information towards diagnosis and stage status of diseases. However, cutaneous vessels are highly variable in shape, size, color and architecture, which complicate the detection task. Considering the large variability of these structures, conventional vessel detection techniques lack the generalizability to detect different vessel types and require separate algorithms to be designed for each type. Furthermore, such techniques are highly dependent on precise hand-crafted features which are time-consuming and computationally inefficient. As a solution, we propose a data-driven feature learning framework based on stacked sparse auto-encoders (SSAE) for comprehensive detection of cutaneous vessels. Each training image is divided into small patches of either containing or non-containing vasculature. A multilayer SSAE is designed to learn hidden features of the data in hierarchical layers in an unsupervised manner. The high-level learned features are subsequently fed into a classifier which categorizes each patch into absence or presence of vasculature and localizes vessels within the lesion. Over a test set of 3095 patches derived from 200 images, the proposed framework demonstrated superior performance of 95.4% detection accuracy over a variety of vessel patterns; outperforming other techniques by achieving the highest positive predictive value of 94.7%. The proposed Computer-Aided Diagnosis (CAD) framework can serve as a decision support system assisting dermatologists for more accurate diagnosis, especially in teledermatology applications in remote areas.
机译：皮肤的血管结构是一种重要的生物标志物，诊断和评估皮肤病。损伤血管的存在和分布与特定异常相关。因此，皮肤血管的检测和定位提供了涉及疾病诊断和阶段状态的关键信息。然而，皮肤血管在形状，尺寸，颜色和架构中具有高度变化，这使得检测任务复杂化。考虑到这些结构的大可变性，传统的血管检测技术缺乏检测不同血管类型的概括性，并且需要为每种类型设计单独的算法。此外，这种技术高度依赖于精确的手工制作特征，这些特征是耗时和计算效率低的。作为解决方案，我们提出了一种基于堆积的稀疏自动编码器（SSAE）的数据驱动特征学习框架，以全面检测皮肤血管。每个训练图像分为含有或非含有脉管系统的小斑块。多层SSAE旨在以无监督的方式学习分层层中数据的隐藏特征。随后将高级学习特征送入分类器，该分类器将每个补丁分类为病变内的脉管系统和定位血管的不存在或存在。在源自200张图像的3095个贴片的测试集中，所提出的框架在各种容器图案上显示出95.4％的检测精度的卓越性能;通过实现94.7％的最高阳性预测值来表现出其他技术。提出的计算机辅助诊断（CAD）框架可以作为辅助皮肤科医生进行更准确的诊断的决策支持系统，特别是在远程区域的Telepermatology应用中。

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