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Cardiovascular disease is a leading cause of death in the Western world. The exploration of strategies to enhance the regenerative capacity of the mammalian heart is therefore of great interest. One approach is the treatment of isolated transgenic mouse cardiomyocytes (CMs) with potentially cell cycle-inducing substances and assessment if this results in atypical cell cycle activity or authentic cell division. This requires the tedious and cost intensive manual analysis of microscopy images. Advances in recent years have led to the increasing use of deep learning (DL) algorithms in cell biological image analysis. While developments in image or single-cell classification are well advanced, multi-cell classification in crowded image scenarios remains a challenge. This is reinforced by typically smaller dataset sizes in such laboratory-specific analyses. In this paper, we propose a modular DL-based image analysis pipeline for multi-cell classification of mononuclear and binuclear CMs in confocal microscopy imaging data. We trisect the pipeline structure into preprocessing, modelling and postprocessing. We perform semantic segmentation to extract general image features, which are further analyzed in postprocessing. We benchmark 18 encoder-decoder model architectures, perform hyperparameter optimization, and conduct 127 experiments to evaluate dataset-related effects. The results show that our approach has great potential for automating specific cell culture analyses even with small datasets.
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Friday 8th July
Poster Session 3.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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