Kurugol Sila, Rajadhyaksha Milind, Dy Jennifer G, Brooks Dana H
Electrical and Comp. Eng., Northeastern University, 360 Huntington Av., Boston, MA.
Dermatology Service, Memorial Sloan Kettering Cancer Cnt., 160 East 53 St., New York, NY.
Proc SPIE Int Soc Opt Eng. 2012 Feb 9;8207. doi: 10.1117/12.909227.
Reflectance confocal microscopy (RCM) has seen increasing clinical application for noninvasive diagnosis of skin cancer. Identifying the location of the dermal-epidermal junction (DEJ) in the image stacks is key for effective clinical imaging. For example, one clinical imaging procedure acquires a dense stack of 0.5×0.5mm FOV images and then, after manual determination of DEJ depth, collects a 5×5mm mosaic at that depth for diagnosis. However, especially in lightly pigmented skin, RCM images have low contrast at the DEJ which makes repeatable, objective visual identification challenging. We have previously published proof of concept for an automated algorithm for DEJ detection in both highly- and lightly-pigmented skin types based on sequential feature segmentation and classification. In lightly-pigmented skin the change of skin texture with depth was detected by the algorithm and used to locate the DEJ. Here we report on further validation of our algorithm on a more extensive collection of 24 image stacks (15 fair skin, 9 dark skin). We compare algorithm performance against classification by three clinical experts. We also evaluate inter-expert consistency among the experts. The average correlation across experts was 0.81 for lightly pigmented skin, indicating the difficulty of the problem. The algorithm achieved epidermis/dermis misclassification rates smaller than 10% (based on 25×25 mm tiles) and average distance from the expert labeled boundaries of ~6.4 μm for fair skin and ~5.3 μm for dark skin, well within average cell size and less than 2x the instrument resolution in the optical axis.
反射共聚焦显微镜（RCM）在皮肤癌的无创诊断中的临床应用日益增加。识别图像堆栈中真皮-表皮交界处（DEJ）的位置是有效临床成像的关键。例如，一种临床成像程序获取一系列0.5×0.5mm视野的密集图像堆栈，然后在手动确定DEJ深度后，在该深度收集一个5×5mm的拼接图像用于诊断。然而，特别是在色素较浅的皮肤中，RCM图像在DEJ处的对比度较低，这使得可重复的、客观的视觉识别具有挑战性。我们之前已经发表了基于顺序特征分割和分类的在高色素和低色素皮肤类型中自动检测DEJ算法的概念验证。在低色素皮肤中，该算法检测到皮肤纹理随深度的变化并用于定位DEJ。在此，我们报告在更广泛的24个图像堆栈（15个白皙皮肤、9个深色皮肤）上对我们算法的进一步验证。我们将算法性能与三位临床专家的分类进行比较。我们还评估了专家之间的一致性。对于色素较浅的皮肤，专家之间的平均相关性为0.81，表明该问题具有难度。该算法实现的表皮/真皮误分类率小于10%（基于25×25mm切片），对于白皙皮肤，与专家标记边界的平均距离约为6.4μm，对于深色皮肤约为5.3μm，完全在平均细胞大小范围内，且在光轴上小于仪器分辨率的2倍。
