# Robust consensus nuclear and cell segmentation

## TL;DR

This paper introduces CellSampler, a tool that combines multiple cell segmentation methods to create a more accurate and optimized result.

## Contribution

The novelty is a software wrapper that generates an 'uber mask' by combining and optimizing multiple segmentation techniques.

## Key findings

- CellSampler combines multiple segmentation masks into a single optimized mask.
- The 'uber mask' is determined based on user-defined neighborhood sizes and statistical measures.

## Abstract

Cell segmentation is a crucial step in numerous biomedical imaging endeavors—so much so that the community is flooded with publicly available, state-of-the-art segmentation techniques ready for out-of-the-box use. Assessing the strengths and limitations of each method on a tissue sample set and then selecting the optimal method for each research objective and input image are time-consuming and exacting tasks that often monopolize the resources of biologists, biochemists, immunologists, and pathologists, despite not being the primary goal of their research projects. In this work, we present a segmentation software wrapper, coined CellSampler, which runs a selection of established segmentation methods and then combines their individual segmentation masks into a single optimized mask. This so-called “uber mask” selects the best of the established masks across local neighborhoods within the image, where both the neighborhood size and the statistical measure used to define what qualifies as “best” are user-defined.