Work

Author response: CellSeg3D: self-supervised 3D cell segmentation for light-sheet microscopy

Year: 2024

Type: peer-review

Abstract: Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a novel 3D self-supervised learning method designed to address the inherent complexity of quantifying cells in 3D volumes, often in cleared neural tissue. We offer a new 3D mesoSPIM dataset and show that CellSeg3D can match state-of-the-art supervised methods. Our contributions are made accessible through a Python package with full GUI integration in napari.

Source: eLife

DOI: 10.7554/elife.99848.2.sa0

Authors: Cyril Achard, Timokleia Kousi, Markus Frey, Maxime Vidal, Yves Paychère, Colin Hofmann, Asim Iqbal, Sebastien B Hausmann, Stéphane Pagès, Mackenzie Weygandt Mathis

Institutions: École Polytechnique Fédérale de Lausanne, Wyss Center for Bio and Neuroengineering

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $2,500

OpenAlex ID: W4405759154

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