Evolutionary Grid Optimization and Deep Learning for Improved In Vitro Cellular Spheroid Localization
Source abstract
Abstract
The recently developed high-throughput system for cell spheroid generation (SpheroWell) is a promising technology for cost- and time-efficient in vitro analysis of, for example, chondrogenic differentiation. It is a compartmental growth surface where spheroids develop from a cell monolayer by self-assembling and aggregation. In order to automatize the analysis of spheroids, we aimed to develop imaging software and improve the localization of cell compartments and fully formed spheroids. Our workflow provides automated detection and localization of spheroids in different formation stages within Petri dishes based on images created with a low-budget camera imaging setup. This automated detection enables a fast and inexpensive analysis workflow by processing a stack of images within a short period of time, which is essential for the extraction of early readout parameters. Our workflow combines image processing algorithms and deep learning-based image localization/segmentation methods like Mask R-CNN and Unet++. These methods are refined by an evolution strategy for automated grid detection, which is able to improve the overall segmentation and classification quality. Besides the already pre-trained neural networks and predefined image processing parameters, our evolution-based post-processing provides the required adaptability for our workflow to deliver a consistent and reproducible quality. This is especially important due to the use of a low-budget imaging setup with various light conditions. The to-be-detected objects of the three different stages show improved results using our evolutionary post-processing for monolayer and starting aggregation with Dice coefficients of 0.7301 and 0.8562, respectively, compared with the raw scores of 0.2879 and 0.8187. The Dice coefficient of the fully formed spheroids in both cases is 0.8829. With our algorithm, we provide automated analyses of cell spheroid by self-assembling in SpheroWell dishes, even if the images are created using a low-budget camera setup.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Cell Image Analysis Techniques · 3D Printing in Biomedical Research · Single-cell and spatial transcriptomics
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Bioinformatics Research Group, University of Applied Sciences Upper Austria, Softwarepark 11-13, 4232 Hagenberg, Austria
Department of Computer Science, Johannes Kepler University, Altenberger Straße 69, 4040 Linz, Austria
Ludwig Boltzmann Institute for Traumatology, The Research Center in Cooperation with the AUVA Austrian Cluster for Tissue Regeneration, 1200 Vienna, Austria
Department of Orthopedics and Trauma-Surgery, Division of Trauma-Surgery, Medical University of Vienna, 1090 Vienna, Austria
Institute of Computational Perception, Johannes Kepler University, Altenberger Straße 69, 4040 Linz, Austria
Funding
Center of Excellence for Technical Innovation in Medicine (TIMED)
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