Abstract

Quantifying the spatial distribution of immune cells within intact skin tissue is essential for understanding diseases such as vitiligo, in which CD8+ T cells selectively destroy epidermal melanocytes within the discrete, parallelogram-shaped epidermal compartments of mouse tail skin, which we term scales. Existing workflows rely on manual region drawing, which is labor-intensive and operator-dependent. Here we present ScaleNet, a three-stage deep-learning pipeline for automated per-scale quantification of whole-mount immunofluorescent images, implemented as an Imaris XTension to enable seamless integration with existing 3D imaging workflows. ScaleNet (i) encodes a 3D confocal volume as a pseudo-RGB projection that preserves height information lost by standard maximum-intensity projection, (ii) applies two independently trained Detectron2 Mask R-CNN models—one for epidermal scales and one for hair follicles—with sliced inference (SAHI) to segment whole-mount images at full resolution, and (iii) maps the resulting 2D mask back into the Imaris 3D coordinate system to quantify user-defined Spot objects per scale. Applied to vitiligo mice imaging, ScaleNet produced per-scale counts of CD8+ T cells and DCT+ melanocytes, enabling unbiased spatial statistics in the tail epidermis, demonstrating that ScaleNet can provide the quantitative spatial resolution needed to dissect the micro-anatomical dynamics of autoimmune depigmentation.

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