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

TY - GEN

T1 - Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites

AU - Dubost, Florian

AU - Dünnwald, Max

AU - Huff, Denver

AU - Scheumann, Vincent

AU - Schreiber, Frank

AU - Vernooij, Meike

AU - Niessen, Wiro

AU - Skalej, Martin

AU - Schreiber, Stefanie

AU - Oeltze-Jafra, Steffen

AU - de Bruijne, Marleen

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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 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.

AB - 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 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.

KW - Clinical MRI

KW - Deep learning

KW - Perivascular spaces

UR - http://www.scopus.com/inward/record.url?scp=85075574412&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-32695-1_12

DO - 10.1007/978-3-030-32695-1_12

M3 - Article in proceedings

AN - SCOPUS:85075574412

SN - 9783030326944

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 103

EP - 111

BT - 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

A2 - Zhou, Luping

A2 - Sarikaya, Duygu

A2 - Kia, Seyed Mostafa

A2 - Speidel, Stefanie

A2 - Malpani, Anand

A2 - Hashimoto, Daniel

A2 - Habes, Mohamad

A2 - Löfstedt, Tommy

A2 - Ritter, Kerstin

A2 - Wang, Hongzhi

PB - Springer VS

T2 - 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

Y2 - 17 October 2019 through 17 October 2019

ER -