Automatic segmentation and scoring of 3D in vitro skin models using deep learning methods

Anna-Sophia Hertlein; Maximiliane Wußmann; Benjamin Boche; Felix Pracht; Siegfried Holzer; Florian Groeber-Becker; Stefan Wesarg

doi:10.1117/12.3006880

3 April 2024 Automatic segmentation and scoring of 3D in vitro skin models using deep learning methods

Anna-Sophia Hertlein, Maximiliane Wußmann, Benjamin Boche, Felix Pracht, Siegfried Holzer, Florian Groeber-Becker, Stefan Wesarg

Author Affiliations +

Proceedings Volume 12933, Medical Imaging 2024: Digital and Computational Pathology; 129330O (2024) https://doi.org/10.1117/12.3006880
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

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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(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Anna-Sophia Hertlein, Maximiliane Wußmann, Benjamin Boche, Felix Pracht, Siegfried Holzer, Florian Groeber-Becker, and Stefan Wesarg "Automatic segmentation and scoring of 3D in vitro skin models using deep learning methods", Proc. SPIE 12933, Medical Imaging 2024: Digital and Computational Pathology, 129330O (3 April 2024); https://doi.org/10.1117/12.3006880

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