Catena: A Comprehensive Software Suite for Large-Scale Connectomics

Samia Mohinta; Pedro Gómez-Gálvez; Shi Yan Lee; Daniel Franco-Barranco; Michael Clayton; Stephan Preibisch; Jan Funke; Albert Cardona

cs.CVSep 18, 2026

Catena: A Comprehensive Software Suite for Large-Scale Connectomics

Authors: Samia Mohinta, Pedro Gómez-Gálvez, Shi Yan Lee, Daniel Franco-Barranco, Michael Clayton, Stephan Preibisch, Jan Funke, Albert Cardona

Organizations: Dept. of Physiology, Development and Neuroscience, University of Cambridge, UK · MRC Laboratory of Molecular Biology, UK · HHMI Janelia, USA · Donostia International Physics Center (DIPC), San Sebastian, Spain

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

Figures & tables

Figure 1: Schematic of Catena for scalable automated connectome mapping . From (I) raw EM inputs, Catena orchestrates two main stages: (1) Core Modules for segmenting neuron instances and detecting synaptic partners, and (2) Connectome-Enricher Modules for further annotations, including mitochondria segmentation, microtubule tracking and neurotransmitter classification. Catena offers modular, Dockerized runtimes, chunk-wise scaling, pretrained models, and parallel task execution across both local and HPC compute environments.

Figure 2: Proofreading neuron segmentations with Seg2link-3D. False splits are corrected with merge operations (left), and false merges with split operations (right).

Task	Metrics
Neuron segmentation	Variation of Information (VOI) $\downarrow$ , Adapted Rand Index Error (ARAND) $\downarrow$ , Expected Run Length (ERL) $\uparrow$
Synapse detection	F1 $\uparrow$ , Precision $\uparrow$ , Recall $\uparrow$
Microtubule tracking	F1 $\uparrow$ (node and topology), Precision $\uparrow$ , Recall $\uparrow$
Neurotransmitter cls.	F1 $\uparrow$ (per class), Precision $\uparrow$ , Recall $\uparrow$
Mitochondria segmentation	Semantic: IoU $\uparrow$ , Dice $\uparrow$ Instance matching: TP $\uparrow$ /FP $\downarrow$ /FN $\downarrow$ at IoU $\geq 0.5$ association errors: one-to-one $\uparrow$ /over-seg. $\downarrow$ under-seg. $\downarrow$ /many-to-many $\downarrow$ /miss $\downarrow$

Table 1: Primary evaluation metrics per module of Catena.

Figure 3: Scalable, stateful chunk-wise processing in Catena. (1) A terabyte-scale input EM volume is partitioned by Daisy into independent read/write chunks. (2) Pooled chunks are dispatched across a worker pool for distributed execution, with per-chunk status (success, failure, in progress) tracked to enable automatic resumption of failed tasks.

Electron Microscopy

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ConnectomeBench2: A Unified Benchmark for Automated Connectomic Proofreading

Jeff Brown, Tim Farkas, Gleb Razgar, Edward S. Boyden

MIT · The Open University · Mindspan Institute+2

Open paper AskPDF arXiv

Proofreading--correcting segmentation errors in 3D brain reconstructions--is the rate-limiting step in synapse-resolution connectomics. We release ConnectomeBench2, a unified multi-species dataset of over 716,485 expert-labeled proofreading decisions with >4,500,000 associated images spanning four major open connectomes (mouse, human, zebrafish, fly), spanning both split and merge error correction. Trained on this dataset, a single Vision Transformer with shared encoders for mesh geometry and electron microscopy reaches human-level accuracy across species for split error correction and merge error identification, with performance scaling with data size and modality. Beyond accuracy, we show that the model is well-calibrated within distribution, that measures of distribution distance predict where calibration and accuracy will degrade on unseen data, and that connectomics-specific pretraining and active learning-based sample selection show potential to substantially reduce the labeling effort needed to extend to new species and brain regions. The benchmark provides the infrastructure to train and evaluate increasingly capable vision models for connectomic proofreading. Data and code availability. The ConnectomeBench2 dataset is released on Hugging Face at https://huggingface.co/datasets/jeffbbrown2/ConnectomeBench2. The accompanying codebase is available on GitHub at https://github.com/timfarkas/ConnectomeBench2.

Abstract

Electron Microscopy

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