# Deep learning-based segmentation of 2D projection-derived overlapping prospore membrane in yeast

## TL;DR

This paper introduces DeMemSeg, a deep learning tool that accurately segments overlapping prospore membranes in yeast from 2D images, enabling efficient and precise quantitative analysis.

## Contribution

DeMemSeg is a novel deep learning pipeline that effectively segments overlapping prospore membranes in 2D projections, achieving accuracy comparable to manual annotation.

## Key findings

- DeMemSeg accurately segments overlapping prospore membranes in 2D images with performance matching expert annotations.
- The model generalizes well to new data, including gip1Δ mutant cells, capturing morphological defects in prospore membranes.
- DeMemSeg provides a robust workflow for quantitative analysis of complex membrane structures from widely used 2D MIP images.

## Abstract

Quantitative morphological analysis is crucial for understanding cellular processes. While 3D Z-stack imaging offers high-resolution data, the complexity of 3D structures makes direct interpretation and manual annotation challenging and time-consuming, especially for large datasets. Maximum Intensity Projection (MIP) is a common strategy to create more interpretable 2D representations, but this inevitably leads to artificial overlaps between structures, significantly hindering accurate automated segmentation of individual instances by conventional methods or standard deep learning tools. To address this critical challenge in 2D projection analysis, we developed DeMemSeg, a deep learning pipeline based on Mask R-CNN, specifically designed to segment overlapping membrane structures, called prospore membranes (PSMs) during yeast sporulation. DeMemSeg was trained on a custom-annotated dataset, leveraging a systematic image processing workflow. Our optimized model accurately identifies and delineates individual, overlapping PSMs, achieving segmentation performance and derived morphological measurements that are statistically indistinguishable from expert manual annotation. Notably, DeMemSeg successfully generalized to segment PSMs from unseen data acquired from gip1Δ mutant cells, capturing the distinct morphological defects in PSMs. DeMemSeg thus provides a robust, automated solution for objective quantitative analysis of complex, overlapping membrane morphologies directly from widely used 2D MIP images, offering a practical tool and adaptable workflow to advance cell biology research.