DaCapo: a modular deep learning framework for scalable 3D image segmentation
This is an incremental tool for researchers in medical imaging or similar fields needing efficient handling of large 3D datasets.
The authors introduced DaCapo, a modular deep learning framework designed to accelerate training and application of existing ML methods on large, near-isotropic 3D image data, aiming to improve access to scalable segmentation.
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.