Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites
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.
KeywordsPerivascular spaces Deep learning Clinical MRI
This work received funding from the Netherlands Organisation for Health Research and Development (ZonMw - Project 104003005) and the federal state of Saxony-Anhalt, Germany (Project I 88).
- 5.Dubost, F., et al.: Weakly Supervised Object Detection with 2D and 3D Regression Neural Networks. arXiv preprint arXiv:1906.01891 (2019)
- 10.Sudre, C.H., et al.: 3D multirater RCNN for multimodal multiclass detection and characterisation of extremely small objects. In: MIDL 2019 (2018)Google Scholar
- 11.van Wijnen, K.M., et al.: Automated lesion detection by regressing intensity-based distance with a neural network. In: MICCAI (2019)Google Scholar
- 13.Zhang, J., Gao, Y., Park, S.H., Zong, X., Lin, W., Shen, D.: Segmentation of perivascular spaces using vascular features and structured random forest from 7T MR image. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 61–68. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-47157-0_8CrossRefGoogle Scholar