DaCapo: a modular deep learning framework for scalable 3D image segmentation

TL;DR

DaCapo is an open-source, modular deep learning framework designed to efficiently train and deploy models for large-scale 3D isotropic image segmentation, enhancing accessibility and scalability in this domain.

Contribution

It introduces a specialized, scalable, and modular deep learning library optimized for large 3D isotropic image segmentation tasks.

Findings

Improved training efficiency for large 3D images

Enhanced experiment management and deployment capabilities

Open-source platform encouraging community contributions

Abstract

DaCapo is a specialized deep learning library tailored to expedite the training and application of existing machine learning approaches on large, near-isotropic image data. In this correspondence, we introduce DaCapo's unique features optimized for this specific domain, highlighting its modular structure, efficient experiment management tools, and scalable deployment capabilities. We discuss its potential to improve access to large-scale, isotropic image segmentation and invite the community to explore and contribute to this open-source initiative.