A Modular Deep Learning Pipeline for Cell Culture Analysis: Investigating the Proliferation of Cardiomyocytes

Lars Leyendecker, Julius Haas, Tobias Piotrowski, Maik Frye, Cora Becker, Bernd K. Fleischmann, Michael Hesse, Robert H. Schmitt

Keywords: Cellular Segmentation, Deep Learning, Cardiovascular Disease

TL;DR: 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.

Abstract: 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. Recent advances have led to an increasing use of deep learning (DL) algorithms in cellular 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. In total, we conduct 173 experiments. We benchmark 18 encoder-decoder model architectures, perform hyperparameter optimization across 28 runs, 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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Paper Type: both

Primary Subject Area: Segmentation

Secondary Subject Area: Detection and Diagnosis

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Code And Data:

Code and data cannot yet be published, as the research project AIxCell (IGF 21361 N) in whose context this work was conducted is still ongoing until 31st August 2022.

The publication was written within the research project AIxCell (IGF 21361 N). The project of the Research Association for Precision Mechanics, Optics and Medical Technology is funded by the Federal Ministry for Economic Affairs and Energy via the AiF within the context of the programme for the promotion of Industrial Cooperative Research (IGF) based on a resolution of the German Bundestag.