Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis

TL;DR

This paper introduces 3D-OSCOS, an algorithm and toolbox for automatic, objective detection and analysis of 3D protein foci in fluorescent microscope images, addressing challenges of proximity, size variability, and noise.

Contribution

The paper presents a novel 3D object detection algorithm, 3D-OSCOS, tailored for fluorescent microscopy images, enabling fully automated and interactive analysis of protein foci.

Findings

Effective detection of closely situated foci

Handles variable sizes and noise levels

Provides visualization and colocalization analysis

Abstract

Since manual object detection is very inaccurate and time consuming, some automatic object detection tools have been developed in recent years. At the moment, there is no image analysis software available which provides an automatic, objective assessment of 3D foci which is generally applicable. Complications arise from discrete foci which are very close or even come in contact to other foci, moreover they are of variable sizes and show variable signal-to-noise, and must be analyzed fully in 3D. Therefore we introduce the 3D-OSCOS (3D-Object Segmentation and Colocalization Analysis based on Spatial statistics) algorithm which is implemented as a user-friendly toolbox for interactive detection of 3D objects and visualization of labeled images.