@inproceedings{f58e65a00b3941edab9692a988a84328,
title = "Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites",
abstract = "Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, and are a marker of cerebral small vessel disease. Most studies use time-consuming and subjective visual scoring to assess these structures. Recently, automated methods to quantify enlarged perivascular spaces have been proposed. Most of these methods have been evaluated only in high resolution scans acquired in controlled research settings. We evaluate and compare two recently published automated methods for the quantification of enlarged perivascular spaces in 76 clinical scans acquired from 9 different scanners. Both methods are neural networks trained on high resolution research scans and are applied without fine-tuning the networks{\textquoteright} parameters. By adapting the preprocessing of clinical scans, regions of interest similar to those computed from research scans can be processed. The first method estimates only the number of PVS, while the second method estimates simultaneously also a high resolution attention map that can be used to detect and segment PVS. The Pearson correlations between visual and automated scores of enlarged perivascular spaces were higher with the second method. With this method, in the centrum semiovale, the correlation was similar to the inter-rater agreement, and also similar to the performance in high resolution research scans. Results were slightly lower than the inter-rater agreement for the hippocampi, and noticeably lower in the basal ganglia. By computing attention maps, we show that the neural networks focus on the enlarged perivascular spaces. Assessing the burden of said structures in the centrum semiovale with the automated scores reached a satisfying performance, could be implemented in the clinic and, e.g., help predict the bleeding risk related to cerebral amyloid angiopathy.",
keywords = "Clinical MRI, Deep learning, Perivascular spaces",
author = "Florian Dubost and Max D{\"u}nnwald and Denver Huff and Vincent Scheumann and Frank Schreiber and Meike Vernooij and Wiro Niessen and Martin Skalej and Stefanie Schreiber and Steffen Oeltze-Jafra and {de Bruijne}, Marleen",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-32695-1_12",
language = "English",
isbn = "9783030326944",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer VS",
pages = "103--111",
editor = "Luping Zhou and Duygu Sarikaya and Kia, {Seyed Mostafa} and Stefanie Speidel and Anand Malpani and Daniel Hashimoto and Mohamad Habes and Tommy L{\"o}fstedt and Kerstin Ritter and Hongzhi Wang",
booktitle = "OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging - 2nd International Workshop, OR 2.0 2019, and 2nd International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Proceedings",
note = "2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019, and the 2nd International Workshop on Machine Learning in Clinical Neuroimaging, MLCN 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 ; Conference date: 17-10-2019 Through 17-10-2019",
}