Improved Automatic Segmentation of White Matter Hyperintensities in MRI Based on Multilevel Lesion Features

Abstract

Brain white matter hyperintensities (WMHs) are linked to increased risk of cerebrovascular and neurodegenerative diseases among the elderly. Consequently, detection and characterization of WMHs are of significant clinical importance. We propose a novel approach for WMH segmentation from multi-contrast MRI where both voxel-based and lesion-based information are used to improve overall performance in both volume-oriented and object-oriented metrics. Our segmentation method (AMOS-2D) consists of four stages following a "generate-and-test" approach: pre-processing, Gaussian white matter (WM) modelling, hierarchical multi-threshold WMH segmentation and object-based WMH filtering using support vector machines. Data from 28 subjects was used in this study covering a wide range of lesion loads. Volumetric T1-weighted images and 2D fluid attenuated inversion recovery (FLAIR) images were used as basis for the WM model and lesion masks defined manually in each subject by experts were used for training and evaluating the proposed method. The method obtained an average agreement (in terms of the Dice similarity coefficient, DSC) with experts equivalent to inter-expert agreement both in terms of WMH number (DSC = 0.637 vs. 0.651) and volume (DSC = 0.743 vs. 0.781). It allowed higher accuracy in detecting WMH compared to alternative methods tested and was further found to be insensitive to WMH lesion burden. Good agreement with expert annotations combined with stable performance largely independent of lesion burden suggests that AMOS-2D will be a valuable tool for fully automated WMH segmentation in patients with cerebrovascular and neurodegenerative pathologies.

[18F]-Flutemetamol Uptake in Cortex and White Matter: Comparison with Cerebrospinal Fluid Biomarkers and [18F]-Fludeoxyglucose

“…An in-house developed object-based supervised machine-learning algorithm was used for automated segmentation and quantification of WMHs based on FLAIR image intensity and masks of tissue types [ 37 ]. For all segmentations, a single investigator (LFK) performed manual inspection of accuracy and edited when necessary.…”

Automatic Segmentation of White Matter Hyperintensities from Brain Magnetic Resonance Images in the Era of Deep Learning and Big Data – A Systematic Review

“…Fourteen studies described a method for false positive removal (Sudre et al, 2017;Manjón et al, 2018;Stone et al, 2016;Moskops et al. 2018;Rincón et al, 2017;Bowles et al, 2017;Li et al, 2018;Ghafoorian et al, 2017;Roy et al, 2015;Zhan et al, 2017;Hong et al, 2020;Fiford et al, 2020;Rachmadi et al, 2020;Ding et al, 2020). Fazekas visual rating scale was used in the validation of the results in nine studies (Rachmadi et al, 2018;Qin et al, 2018;Guerrero et al, 2017;Griffanti et al, 2016;Ling et al, 2018;Moskops et al, 2018;Jiang et al, 2018;Bowles et al, 2017;Rachmadi et al, 2020).…”

“…Samples involving more than 500 individuals were analysed in only two other studies (Griffanti et al, 2016 (n=559); Jiang et al, 2018 (n=566)). Eight studies used small relevant samples (i.e., fewer than 30 individuals with WMH of presumed vascular origin) (Ding et al, 2020;Rachmadi et al, 2020;Rincón et al ,2017;Roy et al, 2015;Stone et al, 2016, Van Opbroek et al, 2015a, 2015bValverde et al, 2017). Three of them used, in addition, data from MS patients (Rachmadi et al, 2020;Van Opbroek et al, 2015a, 2015b.…”

“…Nine of them also used additional datasets or patient data from clinics. Out of 37, twelve studies reported using data from prospective studies or clinics (Atlason et al, 2019;Bowles et al, 2017;Guerrero et al, 2017;Hong et al, 2020;Moeskops et al, 2018;Ling et al, 2018;Park et al, 2018;Qin et al, 2018;Rincón et al, 2017;Roy et al, 2015;Sundaresan et al, 2019;Wang et al, 2015). Four studies used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (Sudre et al, 2017;Dadar et al, 2017a;Rachmadi et al, 2018Rachmadi et al, , 2020, of which only two declared the subset used (Rachmadi et al, 2018(Rachmadi et al, , 2020.…”

“…Data inclusion and exclusion criteria were not explained in 22/37 studies. Of the studies that reported demographic information, five recruited healthy controls (Griffanti et al, 2016;Sundaresan et al, 2019;Damangir et al, 2017;Rincón et al, 2017;Ding et al, 2020). One study stated that the data selection and manipulation were blinded to clinical information (i.e., avoided clinical review bias) (Dadar et al, 2017a).…”