Automatic Generation of Expert-Level Neuron Segmentation Masks from Fluorescence Microscopy Images for Non-Invasive Deep Learning Analysis of Phase-Contrast Images
Abstract
Background & Objective Accurate segmentation of neurons in microscopy images of neuronal cultures is crucial for research on neurodegenerative diseases and neurotoxicity. Manual annotation of such images is time-consuming, subjective, and inconsistent across experts. Deep learning (DL) models offer an effective alternative, but require high-quality training datasets composed of microscopy images with accurately segmented neurons, typically created by experts. Neuronal cultures can be imaged using either phase-contrast or fluorescence microscopy. While live neurons are easily detected in fluorescence images, this modality requires staining, which affects cell viability. Conversely, phase-contrast imaging is non-invasive but makes neuron identification more challenging. Methods In this work we present a robust computer vision pipeline for the automatic generation of expert-level neuron segmentation masks from fluorescence microscopy images. These masks are used to train DL models for neuron identification in phase-contrast images. Our method relies on paired fluorescence and phase-contrast images captured simultaneously at the same magnification and coordinates, ensuring alignment across modalities. Once trained, the DL models can segment neurons in phase-contrast images alone, eliminating the need for fluorescence staining. Results We validate our approach by comparing the automatically generated masks with expert annotations using multiple agreement metrics, including IoU, Accuracy, and Gwet's AC1. Such metrics are calculated not only for the identification but also for the segmentation of instances. Conclusions This study presents a classical computer vision algorithm for generating neuron segmentation masks, which can be used for downstream applications such as non-invasive cell viability analyses by means of DL.
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BibTeX
@misc{villarroyapique2026automatic,
title = {{Automatic Generation of Expert-Level Neuron Segmentation Masks from Fluorescence Microscopy Images for Non-Invasive Deep Learning Analysis of Phase-Contrast Images}},
author = {Gerard Villarroya-Piqué and Víctor M. González and Esther Serrano-Pertierra and M. Teresa Fernandez-Sanchez and Antonello Novelli and Angel Rio-Alvarez},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.34464},
eprint = {2609.34464},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.34464},
note = {CC BY-NC-SA 4.0},
}
RIS
TY - UNPB
TI - Automatic Generation of Expert-Level Neuron Segmentation Masks from Fluorescence Microscopy Images for Non-Invasive Deep Learning Analysis of Phase-Contrast Images
AU - Gerard Villarroya-Piqué
AU - Víctor M. González
AU - Esther Serrano-Pertierra
AU - M. Teresa Fernandez-Sanchez
AU - Antonello Novelli
AU - Angel Rio-Alvarez
PY - 2026
DA - 2026/09/28
PB - arXiv
DO - 10.48550/arxiv.2609.34464
UR - https://arxiv.org/abs/2609.34464
AB - Background & Objective Accurate segmentation of neurons in microscopy images of neuronal cultures is crucial for research on neurodegenerative diseases and neurotoxicity. Manual annotation of such images is time-consuming, subjective, and inconsistent across experts. Deep learning (DL) models offer an effective alternative, but require high-quality training datasets composed of microscopy images with accurately segmented neurons, typically created by experts. Neuronal cultures can be imaged using either phase-contrast or fluorescence microscopy. While live neurons are easily detected in fluorescence images, this modality requires staining, which affects cell viability. Conversely, phase-contrast imaging is non-invasive but makes neuron identification more challenging. Methods In this work we present a robust computer vision pipeline for the automatic generation of expert-level neuron segmentation masks from fluorescence microscopy images. These masks are used to train DL models for neuron identification in phase-contrast images. Our method relies on paired fluorescence and phase-contrast images captured simultaneously at the same magnification and coordinates, ensuring alignment across modalities. Once trained, the DL models can segment neurons in phase-contrast images alone, eliminating the need for fluorescence staining. Results We validate our approach by comparing the automatically generated masks with expert annotations using multiple agreement metrics, including IoU, Accuracy, and Gwet's AC1. Such metrics are calculated not only for the identification but also for the segmentation of instances. Conclusions This study presents a classical computer vision algorithm for generating neuron segmentation masks, which can be used for downstream applications such as non-invasive cell viability analyses by means of DL.
ER -