Hertlein, Anna-Sophia ; Wußmann, Maximiliane ; Boche, Benjamin ; Pracht, Felix ; Holzer, Siegfried ; Groeber-Becker, Florian ; Wesarg, Stefan ; Tomaszewski, John E. ; Ward, Aaron D. (2024)
Automatic segmentation and scoring of 3D in vitro skin models using deep learning methods.
Medical Imaging 2024: Digital and Computational Pathology. San Diego, USA (19.02.-21.02.2024)
doi: 10.1117/12.3006880
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Kurzbeschreibung (Abstract)

Cell-based in vitro skin models are an effective method for testing new medical compounds without any animal harming in the process. Histology serves as a cornerstone for evaluating in vitro models, providing critical insights into their structural integrity and functionality. The recently published BSGC score is a method to assess the quality of in vitro epidermal models, based on visual examination of histopathological images. However, this is very time-consuming and requires a high level of expertise. Therefore, this paper presents a method for automatic evaluation of three-dimensional in vitro epidermal models that involves segmentation and classification of epidermal layers in cross-sectional histopathological images. The input images are first pre-processed and in an initial classification step low-quality skin models are filtered. Subsequently, the individual epidermal strata are segmented and a masked image is generated for each stratum. The strata are scored individually using the masked images with a classification network per stratum. Finally the individual scores are merged into an overall weighted score per image. With an accuracy of 81% for the overall scoring the method provides promising results and allows for significant time savings and less subjectivity compared to the manual scoring process.

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