Classification of Vascular Malformations Based on T2 STIR Magnetic Resonance Imaging

  • Vascular malformations (VMs) are a rare condition. They can be categorized into high-flow and low-flow VMs, which is a challenging task for radiologists. In this work, a very heterogeneous set of MRI images with only rough annotations are used for classification with a convolutional neural network. The main focus is to describe the challenging data set and strategies to deal with such data in terms of preprocessing, annotation usage and choice of the network architecture. We achieved a classification result of 89.47 % F1-score with a 3D ResNet 18.

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Metadaten
Author:Danilo Weber NunesORCiD, Michael Hammer, Simone Hammer, Wibke Uller, Christoph PalmORCiDGND
DOI:https://doi.org/10.1007/978-3-658-36932-3_57
Parent Title (English):Bildverarbeitung für die Medizin 2022: Proceedings, German Workshop on Medical Image Computing, Heidelberg, June 26-28, 2022
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2022
Release Date:2022/04/06
Tag:Deep Learning; Magnetic Resonance Imaging; Vascular Malformations
First Page:267
Last Page:272
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
research focus:Lebenswissenschaften und Ethik
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG