Segmenting time‐lapse phase contrast images of adjacent NIH 3T3 cells

Zhang

Computational and Mathematical Methods in Medicine

et al. 2014

Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA) to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI), in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets.

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images

Zhang

Computational and Mathematical Methods in Medicine

et al. 2014

“…Moreover, in fluorescence imaging, cells need to be either genetically engineered to generate fluorescent proteins or fluorescently labeled to enhance cell boundary information, which modifies cell physiological makeup and may cause unknown change of cellular dynamics. Positive phase contrast images provide relatively high image contrast without any biological modification to cells, which makes it a good alternative for cell image segmentation [14, 18, 30, 32–34]. In positive phase contrast images, cell bodies normally show lower light intensity than the background.…”

Section: Introductionmentioning

confidence: 99%

“…This is widely applied in the so called watershed method [7, 28, 41]. The watershed method often causes over-segmentation and may need complex merging algorithms [32, 42, 43] or by combination of Euclidean distance transform and light intensity information in the areas containing clustered cells [34]. Additionally, even with the successful cell localization in a clump area, current algorithms still could not optimize the detection of the actual boundaries between any two contacting cells.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images

et al. 2015

Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.

Label‐free high temporal resolution assessment of cell proliferation using digital holographic microscopy

Janicke¹,

Kårsnäs²,

Egelberg³

et al. 2017

Cytometry Pt A

Cell proliferation assays are widely applied in biological sciences to understand the effect of drugs over time. However, current methods often assess cell population growth indirectly, that is, the cells are not actually counted. Instead other parameters, for example, the amount of protein, are determined. These methods often also demand phototoxic labels, have low temporal resolution, or employ end-point assays, and frequently are labor intensive. We have developed a robust and label-free kinetic cell proliferation assay with high temporal resolution for adherent cells using digital holographic microscopy (DHM), one of many quantitative phase microscopy techniques. As no labels or stains are required, and only very low intensity illumination is necessary, the technique allows for noninvasive continuous cell counting. Only two image processing settings were adjusted between cell lines, making the assay practical, user friendly, and free of user bias. The developed direct assay was validated by analyzing cell cultures treated with various concentrations of the anti-cancer drug etoposide, a well-established topoisomerase inhibitor that causes DNA damage and leads to programmed cell death. After treatment, the unstained adherent cells were nondestructively imaged every 30 min for 36 h inside a cell incubator. In the recorded time-lapse image sequences, individual cells were automatically identified to provide detailed growth curves and growth rate data of cell number, confluence, and average cell volume. Our results demonstrate how these parameters facilitate a deeper understanding of cell processes than what is achievable with current single-parameter and end-point methods. © 2017 International Society for Advancement of Cytometry.