A cross-center smoothness prior for variational Bayesian brain tissue segmentation

Suppose one is faced with the challenge of tissue segmentation in MR images, without annotators at their center to provide labeled training data. One option is to go to another medical center for a trained classifier. Sadly, tissue classifiers do not generalize well across centers due to voxel intensity shifts caused by center-specific acquisition protocols. However, certain aspects of segmentations, such as spatial smoothness, remain relatively consistent and can be learned separately. Here we present a smoothness prior that is fit to segmentations produced at another medical center. This informative prior is presented to an unsupervised Bayesian model. The model clusters the voxel intensities, such that it produces segmentations that are similarly smooth to those of the other medical center. In addition, the unsupervised Bayesian model is extended to a semi-supervised variant, which needs no visual interpretation of clusters into tissues.Comment: 12 pages, 2 figures, 1 table. Accepted to the International Conference on Information Processing in Medical Imaging (2019

Using the fourth dimension to distinguish between structures for anisotropic diffusion filtering in 4D CT perfusion scans

High resolution 4D (3D+time) cerebral CT perfusion (CTP) scans can be used to create 3D arteriograms (showing only arteries) and venograms (only veins). However, due to the low X-ray radiation dose used for acquiring the CTP scans, they are inherently noisy. In this paper, we propose a time intensity profile similarity (TIPS) anisotropic diffusion method that uses the 4th dimension to distinguish between structures, for reducing noise and enhancing arteries and veins in 4D CTP scans. The method was evaluated on 20 patient CTP scans. An observer study was performed by two radiologists, assessing the arteries and veins in arteriograms and venograms derived from the filtered CTP data, compared to those derived from the original data. Results showed that arteriograms and venograms derived from the filtered CTP data showed more and better visualized small arteries and veins in the majority of the 20 evaluated CTP scans. In conclusion, arteries and veins are separately enhanced and noise is reduced by using the time-intensity profile similarity (fourth dimension) to distinguish between structures for anisotropic diffusion filtering in 4D CT perfusion scans

Evaluation of a deep learning approach for the segmentation of brain tissues and white matter hyperintensities of presumed vascular origin in MRI

Automatic segmentation of brain tissues and white matter hyperintensities of presumed vascular origin (WMH) in MRI of older patients is widely described in the literature. Although brain abnormalities and motion artefacts are common in this age group, most segmentation methods are not evaluated in a setting that includes these items. In the present study, our tissue segmentation method for brain MRI was extended and evaluated for additional WMH segmentation. Furthermore, our method was evaluated in two large cohorts with a realistic variation in brain abnormalities and motion artefacts. The method uses a multi-scale convolutional neural network with a T1-weighted image, a T2-weighted fluid attenuated inversion recovery (FLAIR) image and a T1-weighted inversion recovery (IR) image as input. The method automatically segments white matter (WM), cortical grey matter (cGM), basal ganglia and thalami (BGT), cerebellum (CB), brain stem (BS), lateral ventricular cerebrospinal fluid (lvCSF), peripheral cerebrospinal fluid (pCSF), and WMH. Our method was evaluated quantitatively with images publicly available from the MRBrainS13 challenge (n = 20), quantitatively and qualitatively in relatively healthy older subjects (n = 96), and qualitatively in patients from a memory clinic (n = 110). The method can accurately segment WMH (Overall Dice coefficient in the MRBrainS13 data of 0.67) without compromising performance for tissue segmentations (Overall Dice coefficients in the MRBrainS13 data of 0.87 for WM, 0.85 for cGM, 0.82 for BGT, 0.93 for CB, 0.92 for BS, 0.93 for lvCSF, 0.76 for pCSF). Furthermore, the automatic WMH volumes showed a high correlation with manual WMH volumes (Spearman's ρ = 0.83 for relatively healthy older subjects). In both cohorts, our method produced reliable segmentations (as determined by a human observer) in most images (relatively healthy/memory clinic: tissues 88%/77% reliable, WMH 85%/84% reliable) despite various degrees of brain abnormalities and motion artefacts. In conclusion, this study shows that a convolutional neural network-based segmentation method can accurately segment brain tissues and WMH in MR images of older patients with varying degrees of brain abnormalities and motion artefacts