Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study
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Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study. / Werner, Rene; Schetelig, Daniel; Sothmann, Thilo; Mücke, Eike ; Wilms, Matthias; Cheng, Bastian; Forkert, Nils Daniel.
Bildverarbeitung für die Medizin 2017: Algorithmen - Systeme - Anwendungen. Hrsg. / Klaus Hermann Maier-Hein; Thomas Deserno; Heinz Handels; Thomas Tolxdorff. 1. Aufl. Springer, 2017. S. 161-166 (Informatik aktuell).Publikationen: SCORING: Beitrag in Buch/Sammelwerk › SCORING: Beitrag in Sammelwerk › Forschung › Begutachtung
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TY - CHAP
T1 - Low Rank and Sparse Matrix Decomposition as Stroke Segmentation Prior: Useful or Not? A Random Forest-Based Evaluation Study
AU - Werner, Rene
AU - Schetelig, Daniel
AU - Sothmann, Thilo
AU - Mücke, Eike
AU - Wilms, Matthias
AU - Cheng, Bastian
AU - Forkert, Nils Daniel
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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.
U2 - 10.1007/978-3-662-54345-0_39
DO - 10.1007/978-3-662-54345-0_39
M3 - SCORING: Contribution to collected editions/anthologies
SN - 978-3-662-54344-3
T3 - Informatik aktuell
SP - 161
EP - 166
BT - Bildverarbeitung für die Medizin 2017
A2 - Maier-Hein, Klaus Hermann
A2 - Deserno, Thomas
A2 - Handels, Heinz
A2 - Tolxdorff, Thomas
PB - Springer
ER -