Large Scale Image Segmentation with Structured Loss based Deep Learning for Connectome Reconstruction

Understand

We present a method combining affinity prediction with region agglomeration, which improves significantly upon the state of the art of neuron segmentation from electron microscopy (EM) in accuracy and scalability.

• Our method consists of a 3D U-NET, trained to predict affinities between voxels, followed by iterative region agglomeration.
• We train using a structured loss based on MALIS, encouraging topologically correct segmentations obtained from affinity thresholding.
• Our extension consists of two parts: First, we present a quasi-linear method to compute the loss gradient, improving over the original quadratic algorithm.