A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics
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 c...
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BMC
2025-01-01
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| Series: | BMC Bioinformatics |
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| Online Access: | https://doi.org/10.1186/s12859-024-06031-x |
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| author | Arash Shahbazpoor Shahbazi Farzin Irandoost Reza Mahdavian Seyedehsamaneh Shojaeilangari Abdollah Allahvardi Hossein Naderi-Manesh |
| author_facet | Arash Shahbazpoor Shahbazi Farzin Irandoost Reza Mahdavian Seyedehsamaneh Shojaeilangari Abdollah Allahvardi Hossein Naderi-Manesh |
| author_sort | Arash Shahbazpoor Shahbazi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1d4dea8b1f5a45cf8114881b8ad2cce4 |
| institution | Kabale University |
| issn | 1471-2105 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj-art-1d4dea8b1f5a45cf8114881b8ad2cce42025-01-19T12:40:57ZengBMCBMC Bioinformatics1471-21052025-01-0126112410.1186/s12859-024-06031-xA multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamicsArash Shahbazpoor Shahbazi0Farzin Irandoost1Reza Mahdavian2Seyedehsamaneh Shojaeilangari3Abdollah Allahvardi4Hossein Naderi-Manesh5Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares UniversityDepartment of Physics, Shahid Beheshti University (SBU Physics)Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares UniversityBiomedical Engineering Group, Department of Electrical and Information Technology, Iranian Research Organization for Science and Technology (IROST)Department of Biophysics, Faculty of Biological Sciences, Tarbiat Modares UniversityDepartment of Biophysics, Faculty of Biological Sciences, Tarbiat Modares UniversityAbstract 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.https://doi.org/10.1186/s12859-024-06031-xConvolutional neural network (CNN)Deep learningMSC differentiation dynamicsSegmentationSpheroidStem cells |
| spellingShingle | Arash Shahbazpoor Shahbazi Farzin Irandoost Reza Mahdavian Seyedehsamaneh Shojaeilangari Abdollah Allahvardi Hossein Naderi-Manesh A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics BMC Bioinformatics Convolutional neural network (CNN) Deep learning MSC differentiation dynamics Segmentation Spheroid Stem cells |
| title | A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics |
| title_full | A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics |
| title_fullStr | A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics |
| title_full_unstemmed | A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics |
| title_short | A multi-stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics |
| title_sort | multi stage weakly supervised design for spheroid segmentation to explore mesenchymal stem cell differentiation dynamics |
| topic | Convolutional neural network (CNN) Deep learning MSC differentiation dynamics Segmentation Spheroid Stem cells |
| url | https://doi.org/10.1186/s12859-024-06031-x |
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