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.
| 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): |
