Fast Mitochondria Detection for Connectomics

Vincent Casser, Kai Kang, Hanspeter Pfister, Daniel Haehn

Keywords: Mitochondria Detection, Connectomics, Electron Microscopy, Biomedical Imaging, Image Segmentation

TL;DR: We present a fast and accurate mitochondria detector for electron microscope brain images.

Abstract: High-resolution connectomics data 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 datasets can be petabytes in size. 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 mitochondria benchmark. Our results show an Jaccard index of up to 0.90 with inference times lower than 16ms for a 512x512px image tile. This speed is faster than the acquisition speed of modern electron microscopes, enabling mitochondria detection in real-time. Our detector ranks first for real-time detection when compared to previous works and data, results, and code are openly available.

Track: full conference paper

Paper Type: well-validated application

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