Abstract
Manual ischemic stroke lesion segmentation in MR image data is a time-consuming task subject to inter-rater variability. Reliable automated lesion segmentation is of high interest for clinical trials and research in ischemic stroke. However, recent segmentation challenges (e.g. ISLES 2015) illustrate that current state-of-the-art approaches still lack accuracy and ischemic stroke segmentation remains a complicated problem. Within this context, low rank-&-sparse matrix decomposition (also known as robust PCA, RPCA) and RPCA-based non-linear subject-toatlas registration could provide valuable segmentation prior information. The aim of this study is to evaluate the suitability of RPCA and RPCAbased registration for ischemic stroke segmentation in follow-up FLAIR MR data sets. Building on a top-ranked segmentation approach of ISLES 2015, the performance of RPCA sparse component image information as random forest (RF) feature is evaluated. A comprehensive feature-byfeature comparison of the segmentation performance with and without RPCA sparse component information as RF feature illustrate the potential of low rank-&-sparse decomposition to improve stroke segmentation.
Originalsprache | Englisch |
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Titel | Bildverarbeitung für die Medizin 2017 |
Redakteure/-innen | K.H. Maier-Hein, T.M. Deserno, H. Handels, T. Tolxdorff |
Seitenumfang | 6 |
Herausgeber (Verlag) | Springer Vieweg, Berlin Heidelberg |
Erscheinungsdatum | 01.03.2017 |
Seiten | 161-166 |
ISBN (Print) | 978-3-662-54344-3 |
ISBN (elektronisch) | 978-3-662-54345-0 |
DOIs | |
Publikationsstatus | Veröffentlicht - 01.03.2017 |
Veranstaltung | Workshop on Bildverarbeitung fur die Medizin 2017 - Heidelberg, Deutschland Dauer: 12.03.2017 → 14.03.2017 |