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Funke J.,Polytechnic University of Catalonia | Funke J.,ETH Zurich | Klein J.,ETH Zurich | Moreno-Noguer F.,Polytechnic University of Catalonia | And 2 more authors.
Proceedings - International Symposium on Biomedical Imaging | Year: 2016

Structured learning provides a powerful framework for empirical risk minimization on the predictions of structured models. It allows end-to-end learning of model parameters to minimize an application specific loss function. This framework is particularly well suited for discrete optimization models that are used for neuron reconstruction from anisotropic electron microscopy (EM) volumes. However, current methods are still learning unary potentials by training a classifier that is agnostic about the model it is used in. We believe the reason for that lies in the difficulties of (1) finding a representative training sample, and (2) designing an application specific loss function that captures the quality of a proposed solution. In this paper, we show how to find a representative training sample from human generated ground truth, and propose a loss function that is suitable to minimize topological errors in the reconstruction. We compare different training methods on two challenging EM-datasets. Our structured learning approach shows consistently higher reconstruction accuracy than other current learning methods. © 2016 IEEE.


Buhmann J.M.,ETH Zurich | Gerhard S.,ETH Zurich | Gerhard S.,Janelia Research Campus VA | Cook M.,ETH Zurich | And 2 more authors.
Proceedings - International Symposium on Biomedical Imaging | Year: 2016

For both the automatic and manual reconstruction of neural circuits from electron microscopy (EM) images, the detection and identification of intracellular structures provide useful cues. This is particularly true for microtubules which are indicative of the scaffold of neuronal morphology. However, to our knowledge, the automated reconstruction of microtubules from EM images of neural tissue has received no attention so far. In this paper, we present an automatic method for the tracking of microtubules in 3D EM volumes of neural tissue. We formulate an energy-based model on short candidate segments of microtubules found by a local classifier. We enumerate and score possible links between candidates, in order to find a cost-minimal subset of candidates and links by solving an integer linear program. The model provides a way to incorporate biological priors including both hard constraints (e.g. microtubules are topologically chains of links) and soft constraints (e.g. high curvature is unlikely). We test our method on a challenging EM dataset of Drosophila neural tissue and show that our model reliably tracks microtubules spanning many image sections. © 2016 IEEE.

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