An Efficient Approach for Cell Segmentation in Phase Contrast Microscopy Images

TL;DR

This paper introduces a novel, efficient cell segmentation method for phase contrast microscopy images that combines low-rank sparse decomposition with inverse diffraction pattern filtering, achieving accurate segmentation with reduced computational cost.

Contribution

The paper presents a new model that leverages low-rank and structured sparse decomposition along with inverse diffraction filtering for improved cell segmentation in microscopy images.

Findings

Effective segmentation compared to recent methods

Lower computational complexity

Robust performance across similar background images

Abstract

In this paper, we propose a new model to segment cells in phase contrast microscopy images. Cell images collected from the similar scenario share a similar background. Inspired by this, we separate cells from the background in images by formulating the problem as a low-rank and structured sparse matrix decomposition problem. Then, we propose the inverse diffraction pattern filtering method to further segment individual cells in the images. This is a deconvolution process that has a much lower computational complexity when compared to the other restoration methods. Experiments demonstrate the effectiveness of the proposed model when it is compared with recent works.