Image segmentation techniques for stem cell tracking

Jiang, Ching‐Fen; Tsai, Ka-Pei

doi:10.1109/icassp.2013.6637822

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…The final type is a dynamic deformation tracking approach that involves active contours and level sets . In our pilot study , a deformable model based on the Circular Hough transform was applied to refine the segmented cell contours. The deformation method usually focuses on a unique candidate located around an initial position; when applied to multi‐target tracking, the computational overhead can become extremely expensive.…”

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confidence: 99%

Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

et al. 2015

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Dynamic behavior of stem cells during in vitro development is diverse. Previous cell tracking studies have focused more on cell proliferation than on cell aggregation. However, the enhancement of cell proliferation in association with cell aggregation has been reported. In a previous study, we also demonstrated that the aggregation of adult human mesenchymal stem cells to form three-dimensional (3D) cellular spheroids helped maintain the expression of stemness marker genes in the cells. However, the dynamic behavioral changes triggered by spheroid formation remain to be investigated. A scheme of image processing techniques is proposed to meet this need. A hybridthresholding technique was first developed for efficient segmentation of cell clusters, after which a cell tracking method based on pair-matching with topological constraints was designed. Two morphological indices were derived to track the timing of 3D spheroid formation during the cellular aggregation process. Five cell motility indices measured from single cells and 3D spheroids were then compared. After confirmation of more than 90% correspondence between the results obtained by manual tracking and the proposed methods, an analysis of cellular behavior reveals a significant increase in motility in association with spheroid formation, consistent with a previous report that used a gene expression approach. This study proposed a systematic image analysis method to quantify the dynamic behavior of stem cells for stemness evaluation during cell culturing in vitro. Results demonstrated the validity of the developed platform in investigation of the dynamic behavior of cell aggregation in stem cell cultures in vitro. V C 2015 International Society for Advancement of CytometryKey terms Key terms: stem cells; spheroid formation; cell tracking; dynamic behavior STEM cells can differentiate into diverse specialized cells; this mechanism allows the human body to be repaired and replenished. They have become crucial in modern regenerative medicine, where cells are grown in vitro and transplanted into the human body to help self-recovery (1-3). To maintain the self-renewal ability of stem cells is a great challenge during cell culture, because stem cells tend to lose their stemness-the potential for self-renewal and multilineage differentiation propertiesowing to an insufficient microenvironment. The maintenance of self-renewal potential in adult human mesenchymal stem cells (MSCs) by a biomaterial substrate was first reported in Huang et al. (4). This study showed that when MSCs were cultured on chitosan (CS) membranes and those modified by hyaluronan (CS-HA) they could aggregate to form 3D cellular spheroids. Moreover, these 3D spheroids were found to be more strongly associated with the upregulated expression of stemness marker genes than MSCs in 2D culture. Because of their rich biological content and superior ability to mimic the in vivo environment than 2D cell culture, such multi-cellular

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confidence: 99%

Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

et al. 2015

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“…These cha racteristics may be considered for risks evaluation associated with the safety and efficacy of stem cells. Image-based high-content screening has also become increasingly important in stem cells research in monitoring the changes in phenotype, such as cell morphology and differentiation [102,103].…”

Section: B Stem Cells and Cancer Cells Feature Engineering For Machine Learningmentioning

confidence: 99%

Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

et al. 2021

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The applicability of machine learning-based analysis in the field of biomedical field has been very beneficial in determining the biological mechanism and validation for a wide range of biological scenarios. This approach is also gaining momentum in variou s stem cells research activities, specifically for stem cells characterization and differentiation pattern. The adoption of similar computational approaches to study and assess biosafety and bioefficacy risks of stem cells for clinical application is the n ext progression. In particular where tumorigenicity has been one of the major concerns in stem cells therapy. There are many factors influencing tumorigenicity in stem cells which may be difficult to capture under conventional laboratory settings. In addition, given the possible multifactorial etiology of tumorigenicity, defining a onesize-fits-all strategy to test such risk in stem cells might not be feasible and may compromise stem cells safety and effectiveness in therapy. Given the increase in biological datasets (which is no longer limited to genomic data) and the advancement of health informatics powered by state-of-the-art machine learning algorithms, there exists a potential for practical application in biosafety and bioefficacy of stem cells therap y. Here, we identified relevant machine learning approaches and suggested protocols intended for stem cells research focusing on the possibility of its usage for stem cells biosafety and bioefficacy assessment. Ultimately, generating models that may assist healthcare professionals to make a better-informed decision in stem cell therapy.

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“…Variations in background and imprint sizes due to changes of shot type, shot size and speed of impact are not significant in cell segmentation either. The most popular relevant segmentation techniques include thresholding, edge detection, watershed, partial differential equations, and graph cuts [3,[14][15][16][17][18]. It should be noted that the methods described in this chapter are based on intensity of the image.…”

Section: Thesis Structurementioning

confidence: 99%

Automatic coverage measurement of shot peened panels

Shahid¹

2021

Preprint

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Shot peening is the process of treating metallic surfaces with a regulated blast of shots to increase material strength and durability. Determining the coverage level of the shots is an important parameter in the assessment of the quality of treatment. Traditionally, coverage measurement is performed manually using a magnifying glass, which leads to inefficiency. Despite the proposal for the use of image segmentation techniques for determining the coverage measurement, literature on this topic is not extensively developed. In this thesis, various relevant image segmentation techniques are investigated including thresholding, edge detection, watershed segmentation, active contour, graph cut and neural network. The aim is to develop a generic coverage measurement algorithm, which is accurate and robust to variations in illumination, shot type, coverage level and has real-time capabilities using a simple experimental setup. The results obtained from each method are discussed and compared against a set of relevant performance criteria.

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Image segmentation techniques for stem cell tracking

Cited by 8 publications

References 14 publications

Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

Segmentation and tracking of stem cells in time lapse microscopy to quantify dynamic behavioral changes during spheroid formation

Machine Learning in Stem Cells Research: Application for Biosafety and Bioefficacy Assessment

Automatic coverage measurement of shot peened panels

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