Robust 3D Cell Segmentation: Extending the View of Cellpose

arXiv (Cornell University)

Abstract

Increasing data set sizes of 3D microscopy imaging experiments demand for an automation of segmentation processes to be able to extract meaningful biomedical information. Due to the shortage of annotated 3D image data that can be used for machine learning-based approaches, 3D segmentation approaches are required to be robust and to generalize well to unseen data. The Cellpose approach proposed by Stringer et al. proved to be such a generalist approach for cell instance segmentation tasks. In this paper, we extend the Cellpose approach to improve segmentation accuracy on 3D image data and we further show how the formulation of the gradient maps can be simplified while still being robust and reaching similar segmentation accuracy. The code is publicly available and was integrated into two established open-source applications that allow using the 3D extension of Cellpose without any programming knowledge.

Authors 3

1. Dennis Eschweiler Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision , RWTH Aachen University , Aachen , Germany

2. Richard S. Smith

   John Innes Centre · Norwich Research Park

   Affiliation as printed

   John Innes Centre , Norwich Research Park , Norwich , UK

3. Johannes Stegmaier Aachen Institute of Imaging & Computer Vision

   RWTH Aachen University

   Affiliation as printed

   Institute of Imaging and Computer Vision , RWTH Aachen University , Aachen , Germany