Airway Cells 3D Reconstruction via Manual and Machine-Learning Aided Segmentation of Volume EM Datasets

Author:

Vijayakumaran, Aaran

Abuammar, Analle

Medagedara, Odara

Narayan,Kedar

Mennella, Vito
Author Address

Medical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK., Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, NIH, Bethesda, MD, USA., Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA., Medical Research Council Toxicology Unit, School of Biological Sciences, University of Cambridge, Cambridge, UK. vm430@cam.ac.uk.,

Abstract:

Volume electron microscopy (vEM) is a high-resolution imaging technique capable of revealing the 3D structure of cells, tissues, and model organisms. This imaging modality is gaining prominence due to its ability to provide a comprehensive view of cells at the nanometer scale. The visualization and quantitative analysis of individual subcellular structures however requires segmentation of each 2D electron micrograph slice of the 3D vEM dataset; this process is extremely laborious de facto limiting its applications and throughput. To address these limitations, deep learning approaches have been recently developed including Empanada-Napari plugin, an open-source tool for automated segmentation based on a Panoptic-DeepLab (PDL) architecture. In this chapter, we provide a step-by-step protocol describing the process of manual segmentation using 3dMOD within the IMOD package and the process of automated segmentation using Empanada-Napari plugins for the 3D reconstruction of airway cellular structures. © 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.

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