Catena integrates tools for mapping neuron connections at scale
Catena: A Comprehensive Software Suite for Large-Scale Connectomics
Computer Vision and Pattern Recognition
Summary
Mapping how neurons connect in the brain requires many different software tools that often don’t work well together. The authors created Catena, a complete and open software system that combines all necessary steps like detecting neurons, synapses, and cellular structures, making the process easier and more consistent. Catena uses pre-trained machine learning models to reduce the amount of new data needed and runs reliably across different computers. This helps scientists create detailed maps of brain connections from electron microscope images with less manual effort.
What this means in practice
- •For neuroscience data teams: Process large-scale electron microscopy brain image datasets consistently using Catena’s integrated pipeline for neuron and synapse reconstruction.
- •For microscopy imaging centers: Deploy Catena’s containerized modules to standardize and automate image analysis workflows across diverse computing environments.
Authors
Samia Mohinta, Pedro Gómez-Gálvez, Shi Yan Lee, Daniel Franco-Barranco, Michael Clayton, Stephan Preibisch, Jan Funke, Albert Cardona
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