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

arXiv: https://arxiv.org/abs/2408.02834 · HTML: https://mlsanity.com/paper/2408.02834/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 9 references that are in the map.
- First posted on arXiv 5 Aug 2024.

## Builds on (most cited first, 2 of 2)

- [Superhuman Accuracy on the SNEMI3D Connectomics Challenge](https://mlsanity.com/paper/1706.00120/) (2017)
- [Hot-Distance: Combining One-Hot and Signed Distance Embeddings for Segmentation](https://mlsanity.com/paper/2406.17936/) (2024)

## Built on by (most cited first, 1 of 1)

- [Catena: A Comprehensive Software Suite for Large-Scale Connectomics](https://mlsanity.com/paper/2609.21887/) (2026)

## Abstract (arXiv)

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.