Spherical Harmonics for Shape-Constrained 3D Cell Segmentation

TL;DR

This paper introduces a novel approach using spherical harmonics to incorporate shape priors into neural network-based 3D cell segmentation, improving prediction naturalness and accuracy in microscopy data analysis.

Contribution

It proposes the use of spherical harmonics as an inherent shape constraint in neural networks for 3D cell segmentation, offering an alternative to existing methods.

Findings

Spherical harmonics effectively constrain cell shape predictions.

The approach outperforms some state-of-the-art methods on benchmark datasets.

Analysis of benefits and limitations of spherical harmonic representations.

Abstract

Recent microscopy imaging techniques allow to precisely analyze cell morphology in 3D image data. To process the vast amount of image data generated by current digitized imaging techniques, automated approaches are demanded more than ever. Segmentation approaches used for morphological analyses, however, are often prone to produce unnaturally shaped predictions, which in conclusion could lead to inaccurate experimental outcomes. In order to minimize further manual interaction, shape priors help to constrain the predictions to the set of natural variations. In this paper, we show how spherical harmonics can be used as an alternative way to inherently constrain the predictions of neural networks for the segmentation of cells in 3D microscopy image data. Benefits and limitations of the spherical harmonic representation are analyzed and final results are compared to other state-of-the-art approaches on two different data sets.