P327 - Fast Mitochondria Detection for Connectomics

Vincent Casser, Kai Kang, Hanspeter Pfister, Daniel Haehn

Show abstract - Show schedule - Proceedings - PDF - Reviews - Teaser

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 a 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. Compared to previous work, our detector ranks first for real-time detection and can be used for image alignment. Our data, results, and code are freely available.
Hide abstract

Poster Session #6 - 13:30 - 15:00 UTC-4 (Wednesday)
Hide schedule

Access paper channel

Spotlight presentation

Can't display slides, your browser doesn't support embedding PDFs.

Download slides