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Updated: May 1, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

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MorphoCloud: Democratizing Access to High-Performance Computing for Morphological Data Analysis.

A Murat Maga^1,2, Jean-Christophe Fillion-Robin³

¹Pediatrics, University of Washington, Seattle, Washington, 98152, USA.

|April 13, 2026

Summary

This summary is machine-generated.

MorphoCloud provides accessible, cloud-based computing for 3D morphological analysis, overcoming hardware limitations for researchers at under-resourced institutions. This platform enables high-performance computing via a web browser, democratizing access to advanced scientific tools.

Keywords:

3D digital morphology 3D visualization cloud computing image analysis morphometrics

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Analysis of Multidimensional Microscopy Data Using Cell-ACDC

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Area of Science:

Computational Biology
Morphometrics
Digital Imaging

Background:

Digitization of biological specimens generates large 3D datasets (e.g., microCT scans), revolutionizing morphological studies.
Open-source software (3D Slicer, SlicerMorph) democratizes access, but a compute gap persists due to hardware requirements (high-end GPUs, RAM).
This digital divide limits researchers and students, particularly at Primarily Undergraduate Institutions (PUIs), from utilizing advanced 3D data and software.

Purpose of the Study:

To present MorphoCloud, a platform bridging the hardware barrier for high-performance 3D morphological analysis.
To provide on-demand, research-grade computing environments accessible via a web browser.
To empower researchers at under-resourced institutions by abstracting cloud infrastructure complexities.

Main Methods:

MorphoCloud employs an "IssuesOps" architecture, managing remote workstations via GitHub Issues using natural-language commands.
It leverages GitHub Issues/Actions for front-end/orchestration, JetStream2 for backend compute, and Apache Guacamole for GPU-accelerated desktop delivery.
The platform offers pre-configured SlicerMorph, R/RStudio, and AI segmentation tools (NNInteractive, MEMOs) with persistent storage.

Main Results:

MorphoCloud enables a streamlined lifecycle for remote computing instances.
Users access a persistent storage volume decoupled from the instance.
Specialized "Workshop" instances facilitate bulk provisioning for educational events, ensuring consistent environments for complex 3D workflows.

Conclusions:

MorphoCloud demonstrates that scientific accessibility requires open infrastructure, not just open data and software.
The platform empowers researchers at under-resourced institutions to perform high-performance morphological analysis and AI-assisted segmentation.
By simplifying cloud administration, MorphoCloud democratizes access to advanced computational tools.