Forking Anatomy: How MorphoDepot Applies the Open-Source Development Model to 3D Digital Morphology

TL;DR

MorphoDepot applies open-source development principles to 3D morphological data, enabling collaborative curation, version control, and standardization to enhance data sharing and AI applications in biological sciences.

Contribution

This paper introduces MorphoDepot, a novel framework that adapts the fork-and-contribute model for managing and collaboratively refining 3D anatomical datasets.

Findings

Enables community-driven curation of 3D morphological data

Integrates version control with biological imaging tools

Creates standardized, high-quality datasets for AI training

Abstract

The increasing use of 3D imaging technologies in biological sciences is generating vast repositories of anatomical data, yet significant barriers prevent this data from reaching its full potential in educational and collaborative contexts. While sharing raw CT and MRI scans has become routine, distributing value-added segmented datasets, where anatomical structures are precisely labeled and delineated, remains difficult and rare. Current repositories function primarily as static archives, lacking mechanisms for iterative refinement, community-driven curation, standardized orientation protocols, and the controlled terminology essential for downstream computational applications, including artificial intelligence, to help us analyze and interpret these unprecedented data resources. We introduce MorphoDepot, a framework that adapts the "fork-and-contribute" model, a cornerstone of modern open-source software development, for collaborative management of 3D morphological data. By integrating git version control and GitHub's "social" collaborative infrastructure with 3D Slicer and its SlicerMorph extension, MorphoDepot transforms segmented anatomical datasets from static resources into dynamic, community-curated projects. This approach directly addresses the challenges of distributed collaboration, enforces transparent provenance tracking, and creates high-quality, standardized training data for AI model development. The result is a system that embodies FAIR (Findable, Accessible, Interoperable, and Reusable) data principles while creating powerful new opportunities for remote learning and collaborative science for biological sciences in general and evolutionary morphology in particular.