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Detailed Protocol for Segmentation and Quantification of Overlapping Prospore Membranes using DeMemSeg.

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

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

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|December 11, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces DeMemSeg, a mask R-CNN model for accurately segmenting overlapping yeast prospore membranes (PSMs) in 2D images. This method enables precise quantitative analysis of membrane morphology for biological research.

Keywords:
Cellular morphologyDeep learningInstance segmentationMask R-CNNMicroscopy image processingOverlapping objectsQuantitative image analysisYeast sporulation

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Area of Science:

  • Cell Biology
  • Biophysics
  • Microscopy

Background:

  • Quantitative analysis of biological membrane morphology is crucial for understanding cellular processes.
  • Manual annotation is laborious and subjective, while automated methods struggle with overlapping structures in 2D microscopy images.

Purpose of the Study:

  • To develop a robust and automated method for high-fidelity instance segmentation and quantitative analysis of overlapping prospore membranes (PSMs).
  • To enable accurate and reproducible segmentation of individual, overlapping membrane structures from 2D maximum intensity projections (MIPs).

Main Methods:

  • A step-by-step protocol involving synchronous sporulation induction, 3D fluorescence image acquisition, and conversion to 2D MIPs.
  • Generation of a custom-annotated dataset using a semi-automated pipeline and a CellPose model for single-cell isolation.
  • Training and application of a mask R-CNN-based model, DeMemSeg, for instance segmentation.

Main Results:

  • DeMemSeg achieves high-fidelity instance segmentation of individual, overlapping PSMs from 2D MIPs.
  • The protocol enables extraction of morphological parameters (e.g., length, roundness) for quantitative phenotyping.
  • Accurate differentiation between wild-type and mutant yeast strains based on membrane morphology.

Conclusions:

  • The DeMemSeg framework provides an objective, efficient, and scalable solution for analyzing complex membrane morphologies.
  • This method facilitates detailed quantitative analysis of dynamic membrane structures, particularly yeast PSMs.
  • Offers a significant advancement over manual annotation and conventional automated methods for biological membrane analysis.