Mem-GAN: A Pseudo Membrane Generator for Single-cell Imaging in Fluorescent Microscopy

Abstract

AbstractMotivationFluorescent microscopy imaging is vital to capturing single-cell spatial data, characterizing tissue organization and facilitating comprehensive analysis of cellular state. Advancements in fluorescent microscopy imaging technologies have enabled precise downstream cellular analysis, particularly in cell segmentation. Accurate segmentation of individual cells allows better profiling and understanding of cell properties and behaviors. The majority of existing segmentation methods predominantly concentrate on enhancing segmentation algorithms, and their effectiveness strongly relies on the input stained image quality. Factors such as high cellular density, indistinct cell boundaries, and staining artifacts can result in uneven and low-quality staining, particularly causing missing or unclear membrane staining. These artifacts adversely impact the efficacy of the subsequent cell segmentation methods.ResultsTo tackle this insufficient membrane staining, we propose a novel approach, Mem-GAN, to generate high-quality membranes for cells with missing or weak membranes. Inspired by advanced style transfer techniques in computer vision, Mem-GAN styles the content of the cells with missing or weak membranes into cells with integrated membrane staining. Considering the differences in membrane morphology between epithelial/tumor cells and immune cells, Mem-GAN deals with tumor and immune cells separately, not only enhancing membrane staining for cells with partially weak membrane signals but also generating membranes for cells with only nuclear channels. The proposed Mem-GAN is evaluated using the publicly available CosMx dataset. Experimental results demonstrate significant improvements in image staining quality, more accurate representation of membrane morphology characteristics, and better performance in downstream segmentation tasks. Mem-GAN is flexibly adapted and applied to other spatially resolved transcriptomics datasets, such as MERFISH and FISHseq. Our work provides a new perspective on tackling the challenges in cell segmentation from fluorescent microscopy image restoration.Availability and implementationThe implementation of Mem-GAN is open-source and available at the github repositoryhttps://github.com/OmicsML/Mem-GAN. The interactive webserver-based demo of Mem-GAN can be accessed athttps://omicsml.ai/memgan.