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Consistent Recurrent Neural Networks for 3D Neuron Segmentation

2021-02-01 17:54:46
Felix Gonda, Donglai Wei, Hanspeter Pfister
arXiv_CV
             
Abstract

We present a recurrent network for the 3D reconstruction of neurons that sequentially generates binary masks for every object in an image with spatio-temporal consistency. Our network models consistency in two parts: (i) local, which allows exploring non-occluding and temporally-adjacent object relationships with bi-directional recurrence. (ii) non-local, which allows exploring long-range object relationships in the temporal domain with skip connections. Our proposed network is end-to-end trainable from an input image to a sequence of object masks, and, compared to methods relying on object boundaries, its output does not require post-processing. We evaluate our method on three benchmarks for neuron segmentation and achieved state-of-the-art performance on the SNEMI3D challenge.

Abstract (translated)

URL

https://arxiv.org/abs/2102.01021

PDF

https://arxiv.org/pdf/2102.01021.pdf

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