Active Contour-Based Cell Segmentation During Freezing and Its Application in Cryopreservation

Abstract

Water permeability of the plasma membrane plays an important role in making optimal cryopreservation protocols for different types of cells. To quantify water permeability effectively, automated cell volume segmentation during freezing is necessary. Unfortunately, there exists so far no efficient and accurate segmentation method to handle this kind of image processing task gracefully. The existence of extracellular ice and variable background present significant challenges for most traditional segmentation algorithms. In this paper, we propose a novel approach to reliably extract cells from the extracellular ice, which attaches to or surrounds cells. Our method operates on temporal image sequences and is composed of two steps. First, for each image from the sequence, a greedy search strategy is employed to track approximate locations of cells in motion. Second, we utilize a localized competitive active contour model to obtain the contour of each cell. Based on the first step's result, the initial contour for level set evolution can be determined appropriately, thus considerably easing the pain of initialization for an active contour model. Experimental results demonstrate that the proposed method is efficient and effective in segmenting cells during freezing.

Segmentation of Overlapped Cervical Cells Using Asymmetric Mixture Model and Shape Constraint Level Set Method

“…Parameter Analysis. Our method has three primary parameters α, β, and c in (17). ese parameters help the algorithm implement more effectively.…”

“…In addition, Chang et al [16] introduced a method which incorporates morphology techniques with double threshold to detect cervical cells, and it was able to segment every cell in a short time. Active contour model was also covered in [17] to extract cells' features. Chen et al [18] presented an improved level set method based on combinations of edge, region, and prior information.…”

“…Minimizing (17) depends on finding the steady state solution of the gradient flow equation for ϕ i . See Appendix B for this process.…”

Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review

“…For freezing cell segmentation, Wu et al [191] have presented a local level set model with a competitive constraint to handle touching HeLa cells, which can tackle the heterogeneous intensity. Nath et al [233] have proposed a fast coupled level set method based on the four color theorem to segment cells in phase-contrast microscopy images, and another efficient level set method is reported in [192] for fluorescent cell segmentation, which formulates the energy functionals into a convex problem such that a global solution can be achieved.…”

Object‐Oriented Segmentation of Cell Nuclei in Fluorescence Microscopy Images

“…Level set algorithms commonly employ the pixel gradients to refine the nucleus boundaries found by the shape‐based methods. These algorithms define their energy functions on the gradients and converge the final boundaries by minimizing these energy functions . However, the use of the gradient information may not always be adequate to correctly split the overlapping nuclei due to the following imperfections in pixel values.…”