Pilot study of semiautomated localization of the dermal/epidermal junction in reflectance confocal microscopy images of skin

Abstract

Reflectance confocal microscopy (RCM) continues to be translated toward the detection of skin cancers in vivo. Automated image analysis may help clinicians and accelerate clinical acceptance of RCM. For screening and diagnosis of cancer, the dermal/epidermal junction (DEJ), at which melanomas and basal cell carcinomas originate, is an important feature in skin. In RCM images, the DEJ is marked by optically subtle changes and features and is difficult to detect purely by visual examination. Challenges for automation of DEJ detection include heterogeneity of skin tissue, high inter-, intra-subject variability, and low optical contrast. To cope with these challenges, we propose a semiautomated hybrid sequence segmentation/classification algorithm that partitions z-stacks of tiles into homogeneous segments by fitting a model of skin layer dynamics and then classifies tile segments as epidermis, dermis, or transitional DEJ region using texture features. We evaluate two different training scenarios: 1. training and testing on portions of the same stack; 2. training on one labeled stack and testing on one from a different subject with similar skin type. Initial results demonstrate the detectability of the DEJ in both scenarios with epidermis/dermis misclassification rates smaller than 10% and average distance from the expert labeled boundaries around 8.5 μm.

Figure 1

Left figure shows the DEJ in a vertical histology cross-section image and the middle and right figures show lateral slices from a RCM stack with the epidermis∕dermis boundary marked. The DEJ is a thin membrane, shown with a blue solid line, that separates the epidermis from the dermis. a single layer of basal cells lies directly on the DEJ. The basal cell layer is typically at average depth of 100 μm below the surface in normal skin and 10 to 15 μm in thickness (Ref. 1). (Color online only.)

Figure 2

An example stack (sequence) of 60 tiles is shown, with increasing depth indicated by increasing slice number in the figure. For this stack, an expert evaluator (see Sec. 3 for details) located the epidermis boundary at slice 19 and the dermis boundary at slice 29.

Figure 3

Flow chart of the algorithm.

Figure 4

An example multivariate z-sequence of features. For illustration purposes, only four features are shown. The segment boundaries of the eight segments found by the sequence segmentation algorithm are shown with solid blue vertical lines. The dashed vertical red lines show the epidermis and dermis boundaries located by the expert. (Color online only.)

Figure 5

Left panel shows the tile sequence and an example output of the sequential segmentation algorithm. Right panel shows the resulting epidermis and dermis boundaries (yellow longer horizontal lines) of the combined sequential+classification decision algorithm. (Color online only.)

Figure 6

Surface plot of the epidermis boundary and the dermis boundary in 3D in comparison to the expert labeled boundaries of RCM stack 1 and 2 for scenario 2 (cross training). Top blue (bottom red) surfaces show the expert labeled epidermis (dermis) boundary for (a) RCM stack 1 and (c) RCM stack 2. The colored surfaces indicate the resultant boundaries of the algorithm for (b) RCM stack 1 and (d) RCM stack 2. The color maps indicate the distance from the expert labeled boundary. The z-axis is in micrometers. x and y axes are in pixels, where the pixel spacing is 0.5μm. Flat regions are the masked out wrinkles. For the smooth visualization purpose, the boundaries are plotted after interpolating them twice in 2D with spline interpolation. (Color online only.)

Figure 7

For scenario 1 and 2 and RCM stacks 1 and 2, the figure show the epidermis and dermis boundaries located by the algorithm in comparision to the expert located boundaries for all of the 164 tile-sequences that were processed by the algorithm. The boundaries shown are 2D Gaussian filtered for smoothness as explained in the post-processing step in Sec 2. The dotted vertical lines in (c) indicate the location of the vertical slice shown in Fig. 8b. (Color online only.)

Figure 8

Comparison of expert markings with the algorithm results shown in vertical views y-z (top) and x-z (bottom). The solid line on the left of both (a) and (b) indicate the vertical slice location. Transition region is located by the algorithm in between epidermis algorithm (green) and dermis algorithm (purple) curves. The green (purple) curve is the epidermis (dermis) boundary found by the algorithm. The blue (red) curve is the dermis (epidermis) boundary marked by the expert. If there is no epidermis expert (blue), the expert found no transition region and the upper and lower boundaries coincide. For visualization purposes, algorithm boundaries computed for each tile are linearly interpolated to the same grid (pixel grid) that the expert used in their mark-up. (Color online only.)

Figure 9

A snapshot from the video file which shows the classification results of scenario 2 applied on RCM data stack 1. The left panel shows epidermis and dermis boundary surfaces and a cutting data slice that moves from the top of the stack to the bottom. The right panel shows the original data slice (bottom) and the same slice with the overlayed algorithm results (top). The video starts from a superior slice of the stack, where all regions were either classified as epidermis (red shaded) or were masked out (dark gray shaded) in the preprocessing stage. Then the cutting plane proceeds to deeper slices. Moving deeper in the stack, first the epidermis regions shrink, and the transition regions (light gray shaded) start. Then the transition regions shrink and the dermis regions (blue shaded) start. The deepest slices in the stack include only dermis regions. (MPEG, 21.1MB)