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Deep Learning in Aging Neuroscience

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Front. Neuroinform. | doi: 10.3389/fninf.2019.00033

Evaluation of Enhanced Learning Techniques for Segmenting Ischaemic Stroke Lesions in Brain Magnetic Resonance Perfusion Images using a Convolutional Neural Network Scheme

  • 1School of Informatics, University of Edinburgh, United Kingdom
  • 2University of Edinburgh, United Kingdom

Magnetic resonance (MR) perfusion imaging non-invasively measures cerebral perfusion, which describes the blood's passage through the brain's vascular network. Therefore it is widely used to assess cerebral ischaemia. Convolutional Neural Networks (CNN) constitute the state-of-the-art method in automatic pattern recognition and hence, in segmentation tasks. But none of the CNN architectures developed to date have achieved high accuracy when segmenting ischaemic stroke lesions, being the main reasons their heterogeneity in location, shape, size, image intensity and texture, especially in this imaging modality. We use a freely available CNN framework, developed for MR imaging lesion segmentation, as core algorithm to evaluate the impact of enhanced machine learning techniques, namely data augmentation, transfer learning and post-processing, in the segmentation of stroke lesions using the ISLES 2017 dataset, which contains expert annotated diffusion-weighted perfusion and diffusion brain MRI of 43 stroke patients. Of all the techniques evaluated, data augmentation with binary closing achieved the best results, improving the mean Dice score in 17\% over the baseline model. Consistent with previous works, better performance was obtained in the presence of large lesions.

Keywords: deep learning, ischaemic stroke lesions, Perfusion magnetic resonance imaging, image segmentation, enhanced learning techniques, Data augmentation, Transfer Learning

Received: 01 Feb 2019; Accepted: 23 Apr 2019.

Edited by:

Juan M. Gorriz, University of Granada, Spain

Reviewed by:

Maneul Grana, universidad del pais vasco
Islem Rekik, University of Dundee, United Kingdom  

Copyright: © 2019 Pérez Malla, Valdés Hernández, Rachmadi and Komura. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Maria D. Valdés Hernández, University of Edinburgh, Edinburgh, United Kingdom, M.Valdes-Hernan@ed.ac.uk