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.
@article{arxiv.2408.02834,
title = {DaCapo: a modular deep learning framework for scalable 3D image segmentation},
author = {William Patton and Jeff L. Rhoades and Marwan Zouinkhi and David G. Ackerman and Caroline Malin-Mayor and Diane Adjavon and Larissa Heinrich and Davis Bennett and Yurii Zubov and CellMap Project Team and Aubrey V. Weigel and Jan Funke},
journal= {arXiv preprint arXiv:2408.02834},
year = {2024}
}