Fast Mitochondria Detection for Connectomics
MIDL(2018)
摘要
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.
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