Bridging the Gap: Point Clouds for Merging Neurons in Connectomics
This addresses a specific error in 3D neuron segmentation for connectomics researchers, offering an incremental improvement over existing methods.
The paper tackles the problem of merging split neuron segmentations in connectomics caused by missing image sections by proposing a point cloud-based classification method using CurveNet, achieving strong performance and scalability to larger gaps while maintaining high efficiency with less data.
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 strongly but also scales reasonably to gaps well beyond what other methods have attempted to address. Additionally, our point cloud representations are highly efficient in terms of data, maintaining high performance with an amount of data that would be unfeasible for other methods. We believe that this is an indicator of the viability of using point cloud representations for other proofreading tasks.