Segmentation and Shape Tracking of Whole Fluorescent Cells Based on the Chan–Vese Model

Abstract

We present a fast and robust approach to tracking the evolving shape of whole fluorescent cells in time-lapse series. The proposed tracking scheme involves two steps. First, coherence-enhancing diffusion filtering is applied on each frame to reduce the amount of noise and enhance flow-like structures. Second, the cell boundaries are detected by minimizing the Chan-Vese model in the fast level set-like and graph cut frameworks. To allow simultaneous tracking of multiple cells over time, both frameworks have been integrated with a topological prior exploiting the object indication function. The potential of the proposed tracking scheme and the advantages and disadvantages of both frameworks are demonstrated on 2-D and 3-D time-lapse series of rat adipose-derived mesenchymal stem cells and human lung squamous cell carcinoma cells, respectively.

Quantitative comparison of 3D third harmonic generation and fluorescence microscopy images

“…Anisotropic diffusion filter (ADF) has been one of the standard choices for noise reduction [41,[55][56][57][58] because it is able to remove noise while keeping the object edges sharp. We use the edge-enhancing ADF proposed by Weickert in 1998 [55] to remove image noise, known as the EED model.…”

“…Several methods based on morphological filters and watershed algorithm were proposed to detect cells or cell nuclei in fluorescence images [28][29][30][31][32]. The active contour models [33][34][35][36][37][38][39][40][41][42][43] became popular for automatic cell/nuclei segmentation at the beginning of this century when the classical CV model [33] was proposed. THG images, with Raman and other nonlinear microscopy images, differ from labeled-fluorescence images in their complexity, inherent to their high information density [5,6,20,21,24,25,[44][45][46][47]62].…”

A Guided Tour of Selected Image Processing and Analysis Methods for Fluorescence and Electron Microscopy

“…This framework has been adapted for cell tracking independently by several groups [270], [274]- [276].…”

Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences

“…Such methods have attracted attention both in the cell tracking field [23], [24], [25], [26] as well as in the more general object tracking one [27], [28], [29], [30], [31]. Common techniques in the cell tracking category involve evolving appearance or geometry models from one frame to the next, typically done using active contours [23], [24], [25], [26] or Gaussian Mixture Models [19]. Even though these methods are attractive and mathematically sound, performance suffers from the fact that they only consider a restricted temporal context and therefore cannot guarantee consistency over a whole sequence.…”