# Fast Mitochondria Detection for Connectomics

Source: [Fast Mitochondria Detection for Connectomics](<https://www.sota2.com/research/paper/68fa67cc1e209131cea187ba>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:02:46.151Z

Authors: Vincent Casser, Kai Kang, Hanspeter Pfister, Daniel Haehn

Published: 2018-12-14T16:58:05.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Semantic Segmentation on Lucchi (test)](<https://www.sota2.com/research/sota/semantic-segmentation-on-lucchi-test.md>): Semantic segmentation; Lucchi (test); mIoU: 89.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1812.06024>)

[PDF](<https://arxiv.org/pdf/1812.06024.pdf>)