Hertlein, Anna-SophiaAnna-SophiaHertleinWußmann, MaximilianeMaximilianeWußmannBoche, BenjaminBenjaminBochePracht, FelixFelixPrachtHolzer, SiegfriedSiegfriedHolzerGroeber-Becker, Florian KaiFlorian KaiGroeber-BeckerWesarg, StefanStefanWesarg2024-04-162024-04-162024https://publica.fraunhofer.de/handle/publica/46611310.1117/12.3006880Cell-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.enBranche: HealthcareResearch Line: Computer vision (CV)Research Line: Machine learning (ML)LTA: Machine intelligence, algorithms, and data structures (incl. semantics)LTA: Generation, capture, processing, and output of images and 3D modelsDeep learningTissue segmentationTissue classificationsconference paper