A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics

Abstract

Abstract There is a growing interest in utilizing 3D culture models for stem cell and cancer cell research due to their closer resemblance to in vivo environments. In this study, human mesenchymal stem cells (MSCs) were cultured using adipocytes and osteocytes as differentiative mediums on varying concentrations of chitosan substrate. Light microscopy was employed to capture cell images from the first day to the 21st day of differentiation. Accurate image segmentation is crucial for analyzing the morphological features of the spheroids during the experimental period and for understanding MSC differentiation dynamics for therapeutic applications. Therefore, we developed an innovative, weakly supervised model, aided by convolutional neural networks, to perform label-free spheroid segmentation. Since obtaining pixel-level ground truth labels through manual annotation is labor-intensive, our approach improves the overall quality of the ground-truth map by incorporating a multi-stage process within a weakly supervised learning framework. Additionally, we developed a robust learning scheme for spheroid detection, providing a reliable foundation to study MSC differentiation dynamics. The proposed framework was systematically evaluated using low-resolution microscopic data and challenging, noisy backgrounds. The experimental results demonstrate the effectiveness of our segmentation approach in accurately separating the spheroid from the background. Furthermore, it achieves performance comparable to fully supervised state-of-the-art approaches. To quantitatively evaluate our algorithm, extensive experiments were conducted using available annotated data, confirming the reliability and robustness of our method. Our computationally extracted features can confirm the experimental results regarding alterations in MSC viability, attachment, and differentiation dynamics among the substrates with three concentrations of chitosan used. We observed the formation of more compact spheroids with higher solidity and convex area, resulting improved cell attachment and viability on the 2% chitosan substrate. Additionally, this substrate exhibited a higher propensity for differentiation into osteocytes, as evidenced by the formation of smaller and more ellipsoid spheroids.

Keywords

• Convolutional neural network (CNN)
• Deep learning
• MSC differentiation dynamics
• Segmentation
• Spheroid
• Stem cells