Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites

Florian Dubost, Max Dünnwald, Denver Huff, Vincent Scheurmann, Frank Schreiber, Meike W. Vernooij, Wiro Niessen, Martin Skalej, Stefanie Schreiber, More Authors

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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’ 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 …
Original languageEnglish
Title of host publicationProceedings of 2nd International Workshop on Context-Aware Surgical Theaters,OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, MLCN 2019
EditorsLuping Zhou, Duygu Sarikaya, Seyed Mostafa Kia, Stefanie Speidel, Anand Malpani, Daniel Hashimoto, Mohamad Habes, Tommy Löfstedt, Kerstin Ritter, Hongzhi Wang
PublisherSpringer
Pages103-111
Number of pages9
ISBN (Electronic)978-3-030-32694-4
ISBN (Print)9783030326944
DOIs
Publication statusPublished - 2019
Event2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019: and the 2nd International Workshop on Machine Learning in Clinical Neuroimaging, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 17 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11796 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Workshop

Workshop2nd International Workshop on Context-Aware Surgical Theaters, OR 2.0 2019
CountryChina
CityShenzhen
Period17/10/1917/10/19

Keywords

  • Clinical MRI
  • Deep learning
  • Perivascular spaces

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  • Cite this

    Dubost, F., Dünnwald, M., Huff, D., Scheurmann, V., Schreiber, F., Vernooij, M. W., Niessen, W., Skalej, M., Schreiber, S., & More Authors (2019). Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI across Sites. In L. Zhou, D. Sarikaya, S. M. Kia, S. Speidel, A. Malpani, D. Hashimoto, M. Habes, T. Löfstedt, K. Ritter, & H. Wang (Eds.), Proceedings of 2nd International Workshop on Context-Aware Surgical Theaters,OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging, MLCN 2019 (pp. 103-111). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11796 LNCS). Springer. https://doi.org/10.1007/978-3-030-32695-1_12