Cell-based therapy is an attractive strategy for the long-term management of various chronic diseases. Mesenchymal stem cells (MSCs) are a heterogeneous group of cells that have demonstrated clinically relevant therapeutic effects. The proliferative and therapeutic potential of MSCs can be characterized by the culture quality, which is reflected by their morphological phenotype. Morphological analysis has been a robust method for monitoring culture quality, but visual inspection is subjective and time-consuming. Our goal is to develop an automated algorithm to segment MSCs for an objective, non-invasive, and rapid cell assessment.
We have built an algorithm to segment MSCs using U-Net architecture trained with 71 phase-contrast micro- graphs having 472 cells. MSC culture images are pre-processed and given as inputs to the trained U-Net model that provides a prediction map for cell segmentation. The U-Net output is then post-processed using morphological operations to get rid of false positive cell detections. Results were validated using visual inspection from MSC experts. Our independent test dataset of 36 images consisted of 186 cells. We obtained a sensitivity of 0.742 and a precision of 0.789 for cell detection and a Dice-Sorensen score of 0.823 ± 0.051 for segmentation. The proposed algorithm shows the potential to segment MSCs with accuracy and robustness higher than conventional U-Net. Automated cell segmentation would enable rapid quantification of cytomorphological features and may also drive stem cell quality control processes.
Purpose: Mesenchymal stem cells (MSCs) have demonstrated clinically relevant therapeutic effects for treatment of trauma and chronic diseases. The proliferative potential, immunomodulatory characteristics, and multipotentiality of MSCs in monolayer culture is reflected by their morphological phenotype. Standard techniques to evaluate culture viability are subjective, destructive, or time-consuming. We present an image analysis approach to objectively determine morphological phenotype of MSCs for prediction of culture efficacy.
Approach: The algorithm was trained using phase-contrast micrographs acquired during the early and mid-logarithmic stages of MSC expansion. Cell regions are localized using edge detection, thresholding, and morphological operations, followed by cell marker identification using H-minima transform within each region to differentiate individual cells from cell clusters. Clusters are segmented using marker-controlled watershed to obtain single cells. Morphometric and textural features are extracted to classify cells based on phenotype using machine learning.
Results: Algorithm performance was validated using an independent test dataset of 186 MSCs in 36 culture images. Results show 88% sensitivity and 86% precision for overall cell detection and a mean Sorensen–Dice coefficient of 0.849 ± 0.106 for segmentation per image. The algorithm exhibited an area under the curve of 0.816 (CI95 = 0.769 to 0.886) and 0.787 (CI95 = 0.716 to 0.851) for classifying MSCs according to their phenotype at early and mid-logarithmic expansion, respectively.
Conclusions: The proposed method shows potential to segment and classify low and moderately dense MSCs based on phenotype with high accuracy and robustness. It enables quantifiable and consistent morphology-based quality assessment for various culture protocols to facilitate cytotherapy development.
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.