Abstract

Magnetic resonance imaging (MRI) stays one of the most essential noninvasive methods for brain diagnostics. It allows obtaining the detailed 3D image of the brain, including various types of soft tissues. In this paper, we compare the influence of the multichannel data composition approach on the model's performance. We consider the binary brain tumor segmentation problem evaluating the Dice, Recall and Precision metrics. One common way to process the medical images with the use of neural networks is to use 2D slices as the input. In contrast to the RGB images, there are plenty of methods of how to combine the multi-channel MRI data structure into the common format for ML-based algorithms. After evaluating several possible combinations we demonstrate the most performance improvement by 6-7% in Dice Recall metrics using the pseudo-RGB approach.

Original languageEnglish
Title of host publicationISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728174013
ISBN (Print)978-1-7281-7402-0
DOIs
Publication statusPublished - 1 Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020 - Iowa City, United States
Duration: 4 Apr 2020 → …

Publication series

NameISBI Workshops 2020 - International Symposium on Biomedical Imaging Workshops, Proceedings

Conference

Conference17th IEEE International Symposium on Biomedical Imaging Workshops, ISBI Workshops 2020
CountryUnited States
CityIowa City
Period04.04.2020 → …

Keywords

  • Brain Tumor
  • Deep Learning
  • Multi-channel MRI
  • Neural Network
  • Segmentation

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