Seed-Point Based Geometric Partitioning of Nuclei Clumps

TL;DR

This paper introduces a seed-point based geometric method for partitioning overlapping cell nuclei in microscopy images, improving accuracy over existing software.

Contribution

It presents a novel geometric partitioning technique using seed points and scoring to effectively de-clump nuclei in biological images.

Findings

Outperforms current popular analysis software in nuclei de-clumping.

Successfully tested on 2420 nuclei clumps.

Provides a new approach for automated cell image analysis.

Abstract

When applying automatic analysis of fluorescence or histopathological images of cells, it is necessary to partition, or de-clump, partially overlapping cell nuclei. In this work, I describe a method of partitioning partially overlapping cell nuclei using a seed-point based geometric partitioning. The geometric partitioning creates two different types of cuts, cuts between two boundary vertices and cuts between one boundary vertex and a new vertex introduced to the boundary interior. The cuts are then ranked according to a scoring metric, and the highest scoring cuts are used. This method was tested on a set of 2420 clumps of nuclei and was found to produced better results than current popular analysis software.