Morphological cell image analysis for real-time monitoring of stem cell culture

Sakina Mohammed Mota; Robert E. Rogers; Andrew W. Haskell; Eoin P. McNeill; Maryellen L. Giger; Roland R. Kaunas; Carl A. Gregory; Kristen C. Maitland

doi:10.1117/12.2507469

21 February 2019 Morphological cell image analysis for real-time monitoring of stem cell culture

Sakina Mohammed Mota, Robert E. Rogers, Andrew W. Haskell, Eoin P. McNeill, Maryellen L. Giger, Roland R. Kaunas, Carl A. Gregory, Kristen C. Maitland

Author Affiliations +

Proceedings Volume 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI; 108831I (2019) https://doi.org/10.1117/12.2507469
Event: SPIE BiOS, 2019, San Francisco, California, United States

Abstract

The capability of mesenchymal stem cells (MSCs) to self-renew is reflected by their morphological phenotype. Cells that rapidly self-replicate (RS) are spindle-shaped and fibroblastic, while cells that slowly replicate (SR) are flattened and cuboidal. In addition to slow replication, SR cells lose most of their ability to differentiate into multiple cell lineages and promote tissue repair. Morphological evaluation can be used as a rapid screening technique to monitor culture viability in real-time and minimize the need for time consuming validation assays during expansion. We have developed an image analysis algorithm to quantitatively determine the morphological features with the goal of non-invasive and automated prediction of culture viability. The algorithm includes cell segmentation and classification. Following initial thresholding for cell localization, individual cells are segmented using region-based edge detection while clustered cells are segmented using a marker-based watershed method. In addition, classification of cell phenotype as RS or SR is accomplished using a logistic regression model. Results were validated via visual inspection from twenty individuals trained to evaluate the morphological phenotypes of MSCs. The segmentation algorithm demonstrated an accuracy of 94.03% and a mean Dice-Sorensen score of 0.71 across 15 images containing 67 cells. The classification results for the test dataset demonstrated an accuracy of 83.33%, an AUC of 0.87 +/- 0.08, and an F-measure of 0.87.

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Sakina Mohammed Mota, Robert E. Rogers, Andrew W. Haskell, Eoin P. McNeill, Maryellen L. Giger, Roland R. Kaunas, Carl A. Gregory, and Kristen C. Maitland "Morphological cell image analysis for real-time monitoring of stem cell culture", Proc. SPIE 10883, Three-Dimensional and Multidimensional Microscopy: Image Acquisition and Processing XXVI, 108831I (21 February 2019); https://doi.org/10.1117/12.2507469

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