Semi-supervised 3D neural networks to track iPS cell division in label-free phase contrast time series images (original) (raw)
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Stem cell manufacturing Automated image analysis cell cultures Human induced pluripotent stem cell (hiPSC) processing Deep learning a b s t r a c t Human induced pluripotent stem cells (hiPSCs) have demonstrated great promise for a variety of applications that include cell therapy and regenerative medicine. Production of clinical grade hiPSCs requires reproducible manufacturing methods with stringent quality-controls such as those provided by image-controlled robotic processing systems. In this paper we present an automated image analysis method for identifying and picking hiPSC colonies for clonal expansion using the CellX TM robotic cell processing system. This method couples a light weight deep learning segmentation approach based on the U-Net architecture to automatically segment the hiPSC colonies in full field of view (FOV) high resolution phase contrast images with a standardized approach for suggesting pick locations. The utility of this method is demonstrated using images and data obtained from the CellX TM system where clinical grade hiPSCs were reprogrammed, clonally expanded, and differentiated into retinal organoids for use in treatment of patients with inherited retinal degenerative blindness.
International Journal of Molecular Sciences
Human pluripotent stem cells are promising for a wide range of research and therapeutic purposes. Their maintenance in culture requires the deep control of their pluripotent and clonal status. A non-invasive method for such control involves day-to-day observation of the morphological changes, along with imaging colonies, with the subsequent automatic assessment of colony phenotype using image analysis by machine learning methods. We developed a classifier using a convolutional neural network and applied it to discriminate between images of human embryonic stem cell (hESC) colonies with “good” and “bad” morphological phenotypes associated with a high and low potential for pluripotency and clonality maintenance, respectively. The training dataset included the phase-contrast images of hESC line H9, in which the morphological phenotype of each colony was assessed through visual analysis. The classifier showed a high level of accuracy (89%) in phenotype prediction. By training the classi...
Revolutionary advances in AI and deep learning in recent years have resulted in an upsurge of papers exploring applications within the biomedical field. Within stem cell research, promising results have been reported from analyses of microscopy images to e.g., distinguish between pluripotent stem cells and differentiated cell types derived from stem cells. In this work, we investigated the possibility of using a deep learning model to predict the differentiation stage of pluripotent stem cells undergoing differentiation towards hepatocytes, based on morphological features of cell cultures. We were able to achieve close to perfect classification of images from early and late time points during differentiation, and this aligned very well with the experimental validation of cell identity and function. Our results suggest that deep learning models can distinguish between different cell morphologies, and provide alternative means of semi-automated functional characterization of stem cell...
Computers in Biology and Medicine, 2014
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2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), 2014
The increased use of stem cells to study disease states in vitro has created a need for tools that provide automated, non-invasive, and objective characterization of cell cultures. In this work, we address this need by developing a novel framework for stem cell assessment using time-lapse phase-contrast microscopy and automated texturebased analysis of images. We capture and quantify morphological changes during stem cell colony growth by segmenting each image of the time-lapse sequence into five distinct classes of cells. We apply our automated classification to enable non-invasive estimation of cell doubling time, and demonstrate applications of the presented framework for quantitative assessment of cell culture conditions.
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Author(s): Guan, Benjamin Xueqi | Advisor(s): Bhanu, Bir | Abstract: Human Embryonic Stem Cell (hESC) have a great potential for regenerative medicine to provide treatments for Parkinson’s disease, Huntington’s disease, Type 1 diabetes mellitus, etc. Consequently, hESC are often used as a model in the biological assay to study the effects of chemical agents on the human body. Video analysis plays an important role for biological assays in the field of prenatal toxicology and stem cell differentiation. This thesis introduces machine learning techniques for detection, segmentation and classification for hESC analysis. For the detection, a bio-driven algorithm was used to detect cell regions in hESC images. Cell region detection is essential in stem cell focused analysis. It can prevent background information from contaminating the analysis and put more emphasis on processing the cell region. For the segmentation part, a bio-inspired method was proposed for bleb extraction and analysis...
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THE 6TH INTERNATIONAL CONFERENCE ON CONTROL & SIGNAL PROCESSING, 2019
Embryo development is one of the key factors that provide pregnancy in IVF treatments. The healthy development of the embryo directly affects the realization of the pregnancy. Being able to monitor the development of the embryo increases the rate of conception. It is expected that the embryo will reach a structure of 2-4-8 cells at a specific time with mitosis from a cell. Early or late proliferation from this time indicates that the embryo is unhealthy. In this study, embryo cells were detected with deep learning-based object detector that is Faster Region-based Convolutional Neural Network (Faster R-CNN). ID numbers have given to the detected cells and cells were tracked. With a data structure, the cells have taken an id number. The position change estimations of the cells were performed with Kalman Filter. Hungarian algorithm was used to correlate cells in video frame changes. Cell tracking was performed with the proposed method and division times and cell counts were obtained from time-lapse videos. The Faster R-CNN is trained and tested with Mouse Embryo Tracking Database which is a public embryo database. Faster Region-based Convolutional Neural Networks (Faster R-CNN) have recently emerged as superior for many image detection tasks. In this study, it has been shown that using Faster R-CNN object detector to cell tracking up to 4 cells can achieve competitive results.