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

Authors :
Patton, William
Rhoades, Jeff L.
Zouinkhi, Marwan
Ackerman, David G.
Malin-Mayor, Caroline
Adjavon, Diane
Heinrich, Larissa
Bennett, Davis
Zubov, Yurii
Team, CellMap Project
Weigel, Aubrey V.
Funke, Jan
Publication Year :
2024

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2408.02834
Document Type :
Working Paper