Evidence map›Paper›PMID 41377101›Full record

ArticleBio-protocol2025

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

Shodai Taguchi, Keita Chagi, Hiroki Kawai, Kenji Irie, Yasuyuki Suda

Abstract read
In one paragraph

Article in Bio-protocol, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Shodai TaguchiPh.D. Program in Humanics, School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Ibaraki, Japan.
Keita ChagiResearch and Development Department, LPIXEL Inc., Chiyoda, Tokyo, Japan.
Hiroki KawaiResearch and Development Department, LPIXEL Inc., Chiyoda, Tokyo, Japan.
Kenji IrieLaboratory of Molecular Cell Biology, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.
Yasuyuki SudaLaboratory of Molecular Cell Biology, Institute of Medicine, University of Tsukuba, Tsukuba, Ibaraki, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantitative analysis of biological membrane morphology is essential for understanding fundamental cellular processes such as organelle biogenesis and remodeling. While manual annotation has been the standard for complex structures, it is laborious and subjective, and conventional automated methods often fail to accurately delineate overlapping objects in 2D projected microscopy images. This protocol provides a complete, step-by-step workflow for the quantitative analysis of overlapping prospore membranes (PSMs) in sporulating yeast. The procedure details the synchronous induction of sporulation, acquisition of 3D fluorescence images and their conversion to 2D maximum intensity projections (MIPs), and the generation of a custom-annotated dataset using a semi-automated pipeline. Finally, it outlines the training and application of our mask R-CNN-based model, DeMemSeg, for high-fidelity instance segmentation and the subsequent extraction of morphological parameters. The primary advantage of this protocol is its ability to enable accurate and reproducible segmentation of individual, overlapping membrane structures from widely used 2D MIP images. This framework offers an objective, efficient, and scalable solution for the detailed quantitative analysis of complex membrane morphologies. Key features • Provides a mask R-CNN-based pipeline to accurately segment individual, overlapping membrane structures resulting from 2D maximum intensity projections of 3D image stacks. • Optimized for quantifying the dynamic morphology of yeast prospore membranes (PSMs), a key model system for studying de novo membrane biogenesis. • Presents a complete workflow from single-cell isolation using a custom CellPose model to detailed manual annotation for creating high-quality training datasets. • Enables robust quantitative phenotyping by extracting morphological parameters (e.g., length, roundness) to distinguish subtle differences between wild-type and mutant strains.

Indexed as

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

Identifiers

PMID41377101
PMCPMC12689160

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.