Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data.

ABSTRACT:

Purpose

Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs.

Approach

We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts.

SUBMITTER: Herbsthofer L 

PROVIDER: S-EPMC9709305 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature