Single-cell phase-contrast tomograms data encoded by 3D Zernike descriptors

TL;DR

This paper introduces a quasi lossless compression method for 3D tomographic data of single cells using 3D Zernike descriptors, enabling improved data management and analysis in flow cytometry.

Contribution

The study presents a novel application of 3D Zernike descriptors for efficient compression of single-cell tomograms, facilitating better data handling in high-throughput flow cytometry.

Findings

Achieved effective quasi lossless compression of 3D tomograms

Enabled new data management and computational pipelines

Demonstrated applicability to high-throughput flow cytometry

Abstract

Phase-contrast tomographic flow cytometry combines quantitative 3D analysis of unstained single cells and high-throughput. A crucial issue of this method is the storage and management of the huge amount of 3D tomographic data. Here we show an effective quasi lossless compression of tomograms data through 3D Zernike descriptors, unlocking data management tasks and computational pipelines that were unattainable until now.