Fast Mitochondria Segmentation for Connectomics

In connectomics, scientists create the wiring diagram of a mammalian brain by identifying synaptic connections between neurons in nano-scale electron microscopy images. This allows for the identification of dysfunctional mitochondria which are linked to a variety of diseases such as autism or bipolar. However, manual analysis is not feasible since connectomics datasets can be petabytes in size. To process such large data, we present a fully automatic mitochondria detector based on a modified U-Net architecture that yields high accuracy and fast processing times. We evaluate our method on multiple real-world connectomics datasets, including an improved version of the EPFL Hippocampus mitochondria detection benchmark. Our results show a Jaccard index of up to 0.90 with inference speeds lower than 16ms for a 512x512 image tile. This speed is faster than the acquisition time of modern electron microscopes, allowing mitochondria detection in real-time. Compared to previous work, our detector ranks first among real-time methods and third overall. Our data, results, and code are freely available.