Bridging the Gap: Point Clouds for Merging Neurons in Connectomics

Abstract

In the field of connectomics, a primary problem is that of 3D neuron segmentation. Although deep learning-based methods have achieved remarkable accuracy, errors still exist, especially in regions with image defects. One common type of defect is that of consecutive missing image sections. Here, data is lost along some axis, and the resulting neuron segmentations are split across the gap. To address this problem, we propose a novel method based on point cloud representations of neurons. We formulate the problem as a classification problem and train CurveNet, a state-of-the-art point cloud classification model, to identify which neurons should be merged. We show that our method not only performs well but scales reasonably to large gaps which no other automated method as attempted to solve. Additionally, our point cloud representations are robust to downsampling, allowing us to maintain strong performance with significantly faster training and less GPU memory usage. We believe that this is an indicator of the viability of using point cloud representations for other proofreading tasks.