Autoproof: Automated Segmentation Proofreading for Connectomics
This addresses the high annotation cost in scaling connectomics for neuroscience, offering an incremental improvement by automating parts of existing proofreading workflows.
The paper tackles the bottleneck of manual proofreading in electron microscopy connectomics by using ground-truth data to train a machine learning model, achieving an 80% cost reduction while retaining 90% of the value in guided workflows and automatically merging 200,000 fragments to increase connectivity completion by 1.3% points.
Producing connectomes from electron microscopy (EM) images has historically required a great deal of human proofreading effort. This manual annotation cost is the current bottleneck in scaling EM connectomics, for example, in making larger connectome reconstructions feasible, or in enabling comparative connectomics where multiple related reconstructions are produced. In this work, we propose using the available ground-truth data generated by this manual annotation effort to learn a machine learning model to automate or optimize parts of the required proofreading workflows. We validate our approach on a recent complete reconstruction of the \emph{Drosophila} male central nervous system. We first show our method would allow for obtaining 90\% of the value of a guided proofreading workflow while reducing required cost by 80\%. We then demonstrate a second application for automatically merging many segmentation fragments to proofread neurons. Our system is able to automatically attach 200 thousand fragments, equivalent to four proofreader years of manual work, and increasing the connectivity completion rate of the connectome by 1.3\% points.