Focused Proofreading: Efficiently Extracting Connectomes from Segmented EM Images
This work addresses the scalability issue in connectomics for researchers studying brain circuitry, offering an incremental improvement by optimizing proofreading efforts.
The paper tackles the problem of manually tracing neural circuits from electron microscopy images, which is time-consuming and limits analysis to small brain regions, by introducing a focused proofreading strategy that achieves 3-5x speedups in tracing without compromising circuit quality.
Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming, manual tracing (proofreading) due to the size and intricacy of these image datasets, thus limiting state-of-the-art analysis to very small brain regions. Potential avenues to improve scalability include automatic image segmentation and crowd sourcing, but current efforts have had limited success. In this paper, we propose a new strategy, focused proofreading, that works with automatic segmentation and aims to limit proofreading to the regions of a dataset that are most impactful to the resulting circuit. We then introduce a novel workflow, which exploits biological information such as synapses, and apply it to a large dataset in the fly optic lobe. With our techniques, we achieve significant tracing speedups of 3-5x without sacrificing the quality of the resulting circuit. Furthermore, our methodology makes the task of proofreading much more accessible and hence potentially enhances the effectiveness of crowd sourcing.