Lauenburg Leander, Troidl Jakob, Gohain Adam, Lin Zudi, Pfister Hanspeter, Wei Donglai
bioRxiv. 2025 Aug 12:2025.08.09.669342. doi: 10.1101/2025.08.09.669342.
Connectomics, a subfield of neuroscience, aims to map and analyze synapse-level wiring diagrams of the nervous system. While recent advances in deep learning have accelerated automated neuron and synapse segmentation, reconstructing accurate connectomes still demands extensive human proofreading to correct segmentation errors. We present SynAnno, an interactive tool designed to streamline and enhance the proofreading of synaptic annotations in large-scale connectomics datasets. SynAnno integrates into existing neuroscience workflows by enabling guided, neuron-centric proofreading. To address the challenges posed by the complex spatial branching of neurons, it introduces a structured workflow with an optimized traversal path and a 3D mini-map for tracking progress. In addition, SynAnno incorporates fine-tuned machine learning models to assist with error detection and correction, reducing the manual burden and increasing proofreading efficiency. We evaluate SynAnno through a user and case study involving seven neuroscience experts. Results show that SynAnno significantly accelerates synapse proofreading while reducing cognitive load and annotation errors through structured guidance and visualization support. The source code and interactive demo are available at: https://github.com/PytorchConnectomics/SynAnno .
连接组学是神经科学的一个子领域，旨在绘制和分析神经系统的突触级布线图。虽然深度学习的最新进展加速了神经元和突触分割的自动化，但重建准确的连接组仍然需要大量人工校对来纠正分割错误。我们展示了SynAnno，这是一个交互式工具，旨在简化和增强大规模连接组学数据集中突触注释的校对工作。SynAnno通过实现以神经元为中心的引导式校对，融入现有的神经科学工作流程。为应对神经元复杂空间分支带来的挑战，它引入了一种结构化工作流程，具有优化的遍历路径和用于跟踪进度的3D迷你地图。此外，SynAnno整合了经过微调的机器学习模型，以协助错误检测和纠正，减轻人工负担并提高校对效率。我们通过涉及七位神经科学专家的用户和案例研究对SynAnno进行了评估。结果表明，SynAnno通过结构化指导和可视化支持，显著加速了突触校对，同时减少了认知负荷和注释错误。源代码和交互式演示可在以下网址获取：https://github.com/PytorchConnectomics/SynAnno 。
