White matter hyperintensities segmentation: a new semi-automated method

White matter hyperintensities (WMH) are brain areas of increased signal on T2-weighted or fluid-attenuated inverse recovery magnetic resonance imaging (MRI) scans. In this study we present a new semi-automated method to measure WMH load that is based on the segmentation of the intensity histogram of fluid-attenuated inversion recovery images. Thirty patients with mild cognitive impairment with variable WMH load were enrolled. The semi-automated WMH segmentation included removal of non-brain tissue, spatial normalization, removal of cerebellum and brain stem, spatial filtering, thresholding to segment probable WMH, manual editing for correction of false positives and negatives, generation of WMH map, and volumetric estimation of the WMH load. Accuracy was quantitatively evaluated by comparing semi-automated and manual WMH segmentations performed by two independent raters. Differences between the two procedures were assessed using Student’s t-tests and similarity was evaluated using linear regression model and Dice similarity coefficient (DSC). The volumes of the manual and semi-automated segmentations did not statistically differ (t-value = -1.79, DF = 29, p = 0.839 for rater 1; t-value = 1.113, DF = 29, p = 0.2749 for rater 2), were highly correlated [R(2) = 0.921, F((1,29)) = 155.54, p < 0.0001 for rater 1; R(2) = 0.935, F((1,29)) = 402.709, p < 0.0001 for rater 2] and showed a very strong spatial similarity (mean DSC = 0.78, for rater 1 and 0.77 for rater 2). In conclusion, our semi-automated method to measure the load of WMH is highly reliable and could represent a good tool that could be easily implemented in routinely neuroimaging analyses to map clinical consequences of WMH

REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

Validation of White Matter Hyperintensities automatic segmentation methods

Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona, Any: 2020, Tutor: Eloi Puertas i Prats i Joaquim Raduà[en] This master’s thesis seeks to review and objectively evaluate the current white matter hyperintensities (WMH) automatic segmentation methods published journals. To this end, the methods have been systematically searched in scientific databases, and those meeting inclusion criteria have been evaluated. The evaluation has consisted in applying the method to detect WMH in our dataset of patients with bipolar disorder and healthy controls, in which an experienced neuroradiologist had manually coded all WMH. After the systematic search, we selected all available methods that were ready for use with standard MRI data by a standard user. Four methods met these criteria. We then applied these methods to detect WMH in our dataset, and compared the results with the neuroradiologist-based ground truth deriving several evaluation metrics. This master’s thesis also include a discussion section, in which we compare the results of our evaluations with the results of the WMH Segmentation Challenge held in 2017, which included substantially different datasets. The most relevant conclusion of this master’s thesis is that no method seems to be accurate enough for clinical implementation, although the low performance of the methods may be related to the differences between our data and the data that were used to train them. Besides, realizing the huge improvement made in the field during the last few years after the appearance of deep neural networks, we anticipate that a method with sufficient accuracy might be available soon. The codes used to obtain the results and graphs displayed in this project together with some guidelines to run them are available through PFM-WMH 1

Accuracy and reproducibility of automated white matter hyperintensities segmentation with lesion segmentation tool: A European multi-site 3T study

Brain vascular damage accumulate in aging and often manifest as white matter hyperintensities (WMHs) on MRI. Despite increased interest in automated methods to segment WMHs, a gold standard has not been achieved and their longitudinal reproducibility has been poorly investigated. The aim of present work is to evaluate accuracy and reproducibility of two freely available segmentation algorithms. A harmonized MRI protocol was implemented in 3T-scanners across 13 European sites, each scanning five volunteers twice (test-retest) using 2D-FLAIR. Automated segmentation was performed using Lesion segmentation tool algorithms (LST): the Lesion growth algorithm (LGA) in SPM8 and 12 and the Lesion prediction algorithm (LPA). To assess reproducibility, we applied the LST longitudinal pipeline to the LGA and LPA outputs for both the test and retest scans. We evaluated volumetric and spatial accuracy comparing LGA and LPA with manual tracing, and for reproducibility the test versus retest. Median volume difference between automated WMH and manual segmentations (mL) was −0.22[IQR = 0.50] for LGA-SPM8, −0.12[0.57] for LGA-SPM12, −0.09[0.53] for LPA, while the spatial accuracy (Dice Coefficient) was 0.29[0.31], 0.33[0.26] and 0.41[0.23], respectively. The reproducibility analysis showed a median reproducibility error of 20%[IQR = 41] for LGA-SPM8, 14% [31] for LGA-SPM12 and 10% [27] with the LPA cross-sectional pipeline. Applying the LST longitudinal pipeline, the reproducibility errors were considerably reduced (LGA: 0%[IQR = 0], p < 0.001; LPA: 0% [3], p < 0.001) compared to those derived using the cross-sectional algorithms. The DC using the longitudinal pipeline was excellent (median = 1) for LGA [IQR = 0] and LPA [0.02]. LST algorithms showed moderate accuracy and good reproducibility. Therefore, it can be used as a reliable cross-sectional and longitudinal tool in multi-site studies

BIANCA (Brain Intensity AbNormality Classification Algorithm): a new tool for automated segmentation of white matter hyperintensities

Reliable quantification of white matter hyperintensities of presumed vascular origin (WMHs) is increasingly needed, given the presence of these MRI findings in patients with several neurological and vascular disorders, as well as in elderly healthy subjects. We present BIANCA (Brain Intensity AbNormality Classification Algorithm), a fully automated, supervised method for WMH detection, based on the k-nearest neighbour (k-NN) algorithm. Relative to previous k-NN based segmentation methods, BIANCA offers different options for weighting the spatial information, local spatial intensity averaging, and different options for the choice of the number and location of the training points. BIANCA is multimodal and highly flexible so that the user can adapt the tool to their protocol and specific needs. We optimised and validated BIANCA on two datasets with different MRI protocols and patient populations (a “predominantly neurodegenerative” and a “predominantly vascular” cohort). BIANCA was first optimised on a subset of images for each dataset in terms of overlap and volumetric agreement with a manually segmented WMH mask. The correlation between the volumes extracted with BIANCA (using the optimised set of options), the volumes extracted from the manual masks and visual ratings showed that BIANCA is a valid alternative to manual segmentation. The optimised set of options was then applied to the whole cohorts and the resulting WMH volume estimates showed good correlations with visual ratings and with age. Finally, we performed a reproducibility test, to evaluate the robustness of BIANCA, and compared BIANCA performance against existing methods. Our findings suggest that BIANCA, which will be freely available as part of the FSL package, is a reliable method for automated WMH segmentation in large cross-sectional cohort studies. © 2016 The Author

A large margin algorithm for automated segmentation of white matter hyperintensity

Precise detection and quantification of white matter hyperintensity (WMH) is of great interest in studies of neurological and vascular disorders. In this work, we propose a novel method for automatic WMH segmentation with both supervised and semi-supervised large margin algorithms provided by the framework. The proposed algorithms optimize a kernel based max-margin objective function which aims to maximize the margin between inliers and outliers. We show that the semi-supervised learning problem can be formulated to learn a classifier and label assignment simultaneously, which can be solved efficiently by an iterative algorithm. The model is learned first via the supervised approach and then fine-tuned on a target image by using the semi-supervised algorithm. We evaluate our method on 88 brain fluid-attenuated inversion recovery (FLAIR) magnetic resonance (MR) images from subjects with vascular disease. Quantitative evaluation of the proposed approach shows that it outperforms other well known methods for WMH segmentation