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arXiv · 2408.02834

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

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

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William Patton, Jeff L. Rhoades, Marwan Zouinkhi, David G. Ackerman, Caroline Malin-Mayor, Diane Adjavon, Larissa Heinrich, Davis Bennett, Yurii Zubov, CellMap Project Team, Aubrey V. Weigel, Jan Funke. 2024-08-05. DaCapo: a modular deep learning framework for scalable 3D image segmentation. https://arxiv.org/abs/2408.02834

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