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The protrusion of the cell surface is an initial step for several cellular processes, including cell migration, phagocytosis, and neurite outgrowth. These membrane protrusions are a result of cytoskeletal rearrangement. The most  widely observed cell protrusions include lamellipodia, pseudopodia, filopodia, microvilli, invadopodia, and podosomes. These protrusions can be of two types — static or dynamic.
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Yeast Colony Embedding Method
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Deep learning-based segmentation of 2D projection-derived overlapping prospore membrane in yeast.

Shodai Taguchi1,2,3,4, Keita Chagi4, Hiroki Kawai4

  • 1Ph.D. Program in Humanics, School of Integrative and Global Majors, University of Tsukuba.

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Summary

DeMemSeg, a deep learning tool, accurately segments overlapping membrane structures in 2D microscopy images. This automated segmentation enables precise quantitative analysis of cellular morphology, advancing cell biology research.

Keywords:
cellular morphologydeep learning-based segmentationmembrane structuremicroscopy image processingyeast sporulation

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Area of Science:

  • Cell Biology
  • Biophysics
  • Computational Biology

Background:

  • Quantitative morphological analysis is vital for understanding cellular processes.
  • 3D Z-stack imaging provides high-resolution data but poses challenges for manual annotation and interpretation due to complex 3D structures.
  • Maximum Intensity Projection (MIP) simplifies 2D visualization but causes artificial overlaps, hindering automated segmentation of individual structures.

Purpose of the Study:

  • To develop an automated deep learning pipeline for segmenting overlapping membrane structures in 2D MIP images.
  • To address the limitations of conventional methods in analyzing complex, overlapping cellular morphologies.
  • To enable accurate quantitative morphological analysis from widely used 2D projection images.

Main Methods:

  • Developed DeMemSeg, a deep learning pipeline based on Mask R-CNN.
  • Trained DeMemSeg on a custom-annotated dataset using a systematic image processing workflow.
  • Validated the model's performance against expert manual annotation and on unseen data from gip1Δ mutant cells.

Main Results:

  • DeMemSeg accurately identifies and delineates individual, overlapping prospore membranes (PSMs) in yeast sporulation.
  • Segmentation performance and derived morphological measurements were statistically indistinguishable from expert manual annotation.
  • The model successfully generalized to segment PSMs in gip1Δ mutant cells, capturing morphological defects.

Conclusions:

  • DeMemSeg provides a robust, automated solution for objective quantitative analysis of complex, overlapping membrane morphologies.
  • The pipeline enables accurate analysis directly from 2D MIP images, overcoming limitations of conventional segmentation methods.
  • DeMemSeg offers an adaptable workflow to advance cell biology research by facilitating detailed morphological studies.