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

Abstract

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.

Large population cell characterization using quantitative phase cytometer

“…Finally, an edge‐based active contour method described in Ref. is implemented to expand and refine the obtained segmentation boundary according to the image gradient. To be noted, partial cells on the borders of images are not taken into account since they are incomplete.…”

Human iPSC-derived RPE and retinal organoids reveal impaired alternative splicing of genes involved in pre-mRNA splicing in PRPF31 autosomal dominant retinitis pigmentosa

“…More than 90% of our genes undergo alternative splicing, generating the vast diversity of proteins in the human interactome (Wang et al, 2015). Mutations of spliceosome components would be expected to disrupt splicing function in all cells due to their ubiquitous requirement for splicing.…”

“…Segmented objects, such as noise, those sizes smaller than a user selected value are then removed. Finally, a watershed-based method is applied to the binary image to split clustered cells, details of this method can be found in Wang et al (2015). Because all the pixels of each cell are connected and represented as a single region, an image region property measuring function (regionprops) in MATLAB was used to extract the information of each cell, such as, size, average intensity value and length.…”

Robust Automated detection of Nanocarriers’ Toxicity using Microscopic Image Analysis

“…One of the classical method for segmentation used in the past is Watershed Algorithm [23] [24]. Various improvements and variations of this method were generated and utilized due to its simplicity, speed and easily adjustable distance map factor [22][25] [26].…”