GRED: Graph-Regularized 3D Shape Reconstruction from Highly Anisotropic and Noisy Images

TL;DR

This paper introduces GRED, a graph-regularized method for automated 3D cell nucleus segmentation in highly anisotropic and noisy microscopy images, combining advanced image processing and machine learning techniques.

Contribution

The paper presents GRED, a novel tool that automates 3D cell nucleus segmentation with accuracy comparable to manual annotation, significantly reducing processing time.

Findings

Achieves manual annotation accuracy in nucleus segmentation

Reduces segmentation time substantially

Supports user-friendly GUI for biological research

Abstract

Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cell nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cell nuclei is very time consuming, which remains a bottleneck in large scale biological experiments. In this work we present a tool for automated segmentation of cell nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and supports a friendly graphical user interface (GUI). We show that our tool is as accurate as manual annotation but greatly reduces the time for the registration.