Catena: A Comprehensive Software Suite for Large-Scale Connectomics

2026-09-18

AI Summary

Catena软件套件通过集成多种模块和预训练模型，解决了神经连接组学中数据处理工具碎片化的问题，提供了可重复且可扩展的解决方案。

Abstract

The gold standard datasets for mapping connectomes are electron microscopy volumes of densely labeled neural tissue at nanometer resolution. Yet reconstructing and proofreading neuronal arbors and annotating all synapses requires pipelining multiple software tools that are often fragmented, inconsistently maintained, or proprietary, hindering reproducibility and automation. Here, we introduce Catena, an open-source, comprehensive, developer-centric software suite for connectomics that integrates modules for 3D neuron and organelle segmentation, synapse detection, microtubule tracking, and neurotransmitter inference. Catena organizes its modules in composable, chunk-wise processing pipelines in a completely documented, extensible, and adaptable design. We further reduce compute and ground-truth data requirements with pretrained machine learning models, facilitating fine-tuning. Catena ships fully containerized modules that encapsulate evolving dependencies for consistent execution across workstations and clusters. By consolidating open components, shareable models, and containerized runtimes, Catena delivers a reproducible and scalable approach to mapping cellular connectomes from electron microscopy volumes. Code and documentation: https://github.com/Mohinta2892/catena.git

Problem

• connectomics
• electron microscopy
• neuronal reconstruction
• software tools
• reproducibility

Innovation

• connectomics
• open-source software
• containerized modules
• pretrained machine learning models
• reproducibility