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Abstract #2728

Brain Segmentation in Rodent MR-Images Using Convolutional Neural Networks

Björn Sigurðsson1, Sune Darkner2, Stefan Sommer2, Kristian Nygaard Mortensen1, Simon Sanggaard3, Serhii Kostrikov4, and Maiken Nedergaard1,5

1Center for translational neuromedicine, University of Copenhagen, Copenhagen, Denmark, 2Department of Computer Science, University of Copenhagen, Copenhagen, Denmark, 3Department of Anesthesiology, Yale School of Medicine, New Haven, CT, United States, 4Institut for Mikro- og Nanoteknologi, Technical University of Denmark, Kgs. Lyngby, Denmark, 5Department of Neurosurgery, University of Rochester, Rochester, NY, United States

This study compares two different methods for the task of brain segmentation in rodent MR-images, a convolutional neural network (CNN) and majority voting of a registration based atlas (RBA) , and how limited training data affect their performance. The CNN was implemented in Tensorflow.

The RBA performs better on average when using a training set with fewer than 20 images but the CNN achieves a higher median dice-score with a training set of 19 images.

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