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

Source: [Automatic 3D object detection of Proteins in Fluorescent labeled microscope images with spatial statistical analysis](<https://www.sota2.com/research/paper/68fa62451e209131ce9f8674>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:16:31.033Z

Authors: Ramin Norousi, Volker J. Schmid

Published: 2016-01-06T15:49:49.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1601.01216>)

[PDF](<https://arxiv.org/pdf/1601.01216.pdf>)