皮肤癌是全世界最普遍的癌症之一。随着医学数字化和心灵感应学的出现,超/多光谱成像(HMSI)允许非侵入性,宏观水平的非电离组织评估。
我们旨在总结基于HMSI的大体水平皮肤组织分类和分割的拟议框架和最新趋势。
进行了系统评价,针对基于HMSI的系统,用于在大体病理期间对皮肤病变进行分类和分割,包括黑色素瘤,色素性病变,还有瘀伤.该审查符合2020年系统评价和荟萃分析首选报告项目(PRISMA)指南。对于2010年至2020年发布的合格报告,HMSI收购趋势,预处理,并进行了分析。
用于皮肤组织分类和分割的基于HMSI的框架变化很大。大多数报告实现了简单的图像处理或机器学习,由于训练数据集很小。方法学是在精心策划的数据集上进行评估的,大多数针对黑色素瘤的检测。预处理方案的选择影响了系统的性能。通常应用某种形式的降维以避免HMSI系统中固有的冗余。
要在实践中使用HMSI进行肿瘤边缘检测,系统评估的重点应该转向决策过程的可解释性和稳健性。
Skin cancer is one of the most prevalent cancers worldwide. In the advent of medical digitization and telepathology, hyper/multispectral imaging (HMSI) allows for noninvasive, nonionizing tissue evaluation at a macroscopic level.
We aim to summarize proposed frameworks and recent trends in HMSI-based classification and segmentation of gross-level skin tissue.
A systematic
review was performed, targeting HMSI-based systems for the classification and segmentation of skin lesions during gross pathology, including melanoma, pigmented lesions, and bruises. The
review adhered to the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines. For eligible reports published from 2010 to 2020, trends in HMSI acquisition, preprocessing, and analysis were identified.
HMSI-based frameworks for skin tissue classification and segmentation vary greatly. Most reports implemented simple image processing or machine learning, due to small training datasets. Methodologies were evaluated on heavily curated datasets, with the majority targeting melanoma detection. The choice of preprocessing scheme influenced the performance of the system. Some form of dimension reduction is commonly applied to avoid redundancies that are inherent in HMSI systems.
To use HMSI for tumor margin detection in practice, the focus of system evaluation should shift toward the explainability and robustness of the decision-making process.