https://www.biorxiv.org/content/10.1101/2024.06.06.597812v1
https://github.com/lahammond/RESPAN
Accurate and unbiased reconstructions of neuronal morphology, including quantification of dendritic spine morphology and distribution, are widely used in neuroscience but remain a major roadblock for large-scale analysis. Traditionally, spine analysis has required labor-intensive manual annotation, which is prone to human error and impractical for large 3D datasets. Previous automated tools for reconstructing neuronal morphology and quantitative dendritic spine analysis face challenges in generating accurate results and, following close inspection, often require extensive manual correction. While recent tools leveraging deep learning approaches have substantially increased accuracy, they lack functionality and useful outputs, necessitating additional tools to perform a complete analysis and limiting their utility. In this paper, we describe Restoration Enhanced SPine And Neuron (RESPAN) analysis, a new comprehensive pipeline developed as an open-source, easily deployable solution that harnesses recent advances in deep learning and GPU processing. Our approach demonstrates high accuracy and robustness, validated extensively across a range of imaging modalities for automated dendrite and spine mapping. It also offers extensive visual and tabulated data outputs, including detailed morphological and spatial metrics, dendritic spine classification, and 3D renderings. Additionally, RESPAN includes tools for validating results, ensuring scientific rigor and reproducibility.
Key Points
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RESPAN is a new automated pipeline for restoration and segmentation of several morphological features of neurons including soma, dendritic shaft, and dendritic spines from various in light microscopy imaging datasets.
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The RESPAN processing pipeline includes data preparation, image restoration, image segmentation, and subsequent visualization and plotting of the resulting data, providing a comprehensive, automated solution for mapping dendrites and dendritic spines.
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By providing clear guidance and an easy to use GUI for using RESPAN and additional Python-based tools, including nnU-Net and SelfNet, we ensure state-of-the-art automated neuron analysis is broadly accessible for a range of diverse datasets and applications.
https://www.biorxiv.org/content/10.1101/2024.06.06.597812v1
https://github.com/lahammond/RESPAN
Accurate and unbiased reconstructions of neuronal morphology, including quantification of dendritic spine morphology and distribution, are widely used in neuroscience but remain a major roadblock for large-scale analysis. Traditionally, spine analysis has required labor-intensive manual annotation, which is prone to human error and impractical for large 3D datasets. Previous automated tools for reconstructing neuronal morphology and quantitative dendritic spine analysis face challenges in generating accurate results and, following close inspection, often require extensive manual correction. While recent tools leveraging deep learning approaches have substantially increased accuracy, they lack functionality and useful outputs, necessitating additional tools to perform a complete analysis and limiting their utility. In this paper, we describe Restoration Enhanced SPine And Neuron (RESPAN) analysis, a new comprehensive pipeline developed as an open-source, easily deployable solution that harnesses recent advances in deep learning and GPU processing. Our approach demonstrates high accuracy and robustness, validated extensively across a range of imaging modalities for automated dendrite and spine mapping. It also offers extensive visual and tabulated data outputs, including detailed morphological and spatial metrics, dendritic spine classification, and 3D renderings. Additionally, RESPAN includes tools for validating results, ensuring scientific rigor and reproducibility.
Key Points
RESPAN is a new automated pipeline for restoration and segmentation of several morphological features of neurons including soma, dendritic shaft, and dendritic spines from various in light microscopy imaging datasets.
The RESPAN processing pipeline includes data preparation, image restoration, image segmentation, and subsequent visualization and plotting of the resulting data, providing a comprehensive, automated solution for mapping dendrites and dendritic spines.
By providing clear guidance and an easy to use GUI for using RESPAN and additional Python-based tools, including nnU-Net and SelfNet, we ensure state-of-the-art automated neuron analysis is broadly accessible for a range of diverse datasets and applications.