Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection—a first study

Abstract

Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution.

An interacting replica approach applied to the traveling salesman problem

“…Augmenting Refs. [5], [6], [7], [8], [9], [10], we further also note the more recent work of Ref. [15] in which the authors demonstrate that the inference algorithms based on evolving interactions between replicated solutions in a cavity type approach have better performance in the binary Ising percepton problem.…”

Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

“…Automatic segmentation can be achieved with computational approaches such as multiresolution community detection and morphological filtering and thresholding. [212][213][214] In addition, unsupervised clustering techniques (e.g., K-means clustering) can segment single cells and intracellular compartments for phasor-based lifetime data. 215 Open source packages such as FLIMfit and FLIM-FRET analyzer have been developed for multiple functionalities, including automatic segmentation, lifetime decay fitting, and data visualization.…”

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

“…Several approaches have been then developed recently for detecting multiple small moving subcellular objects. Specific applications include for cell segmentation [194], [195] and nuclei detection [196]- [198]. Comparisons of spot detection methods have been reported in [21], [199].…”