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Background: The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear. Objective: To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS. Methods: A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively. Whole brain, subcortical gray matter (GM), cortical GM, and cerebral white matter (WM) volume fractions were obtained. DMTs were categorized as higher (DMT-H: natalizumab and rituximab) or lower (DMT-L: interferon-beta and glatiramer acetate) efficacy. Follow-up epochs were analyzed if participants had been on a DMT for ⩾6 months prior to baseline and had at least one follow-up MRI while on DMTs in the same category. Results: A total of 86 DMT epochs (DMT-H: n = 32; DMT-L: n = 54) from 78 participants fulfilled the study inclusion criteria. Mean follow-up was 2.4 years. Annualized rates of thalamic (−0.15% vs −0.81%; p = 0.001) and putaminal (−0.27% vs −0.73%; p = 0.001) atrophy were slower during DMT-H compared to DMT-L epochs. These results remained significant in multivariate analyses including demographics, clinical characteristics, and T2 lesion volume. Conclusion: DMT-H treatment may be associated with slower rates of subcortical GM atrophy, especially of the thalamus and putamen. Thalamic and putaminal volumes are promising imaging biomarkers in MS. [ABSTRACT FROM AUTHOR]

Melanoma is the most deadly form of skin cancer and it is costly for dermatologists to screen every patient for melanoma. There is a need for a system to assess the risk of melanoma based on dermatological photographs of a skin lesion. However, the presence of illumination variation in the photographs can have a negative impact on lesion segmentation and classification performance. A novel multistage illumination modeling algorithm is proposed to correct the underlying illumination variation in skin lesion photographs. The first stage is to compute an initial estimate of the illumination map of the photograph using a Monte Carlo nonparametric modeling strategy. The second stage is to obtain a final estimate of the illumination map via a parametric modeling strategy, where the initial nonparametric estimate is used as a prior. Finally, the corrected photograph is obtained using the final illumination map estimate. The proposed algorithm shows better visual, segmentation, and classification results when compared to three other illumination correction algorithms, one of which is designed specifically for lesion analysis. [ABSTRACT FROM PUBLISHER]

Background: The effects of disease-modifying therapies (DMTs) on region-specific brain atrophy in multiple sclerosis (MS) are unclear., Objective: To determine the effects of higher versus lower efficacy DMTs on rates of brain substructure atrophy in MS., Methods: A non-randomized, observational cohort of people with MS followed with annual brain magnetic resonance imaging (MRI) was evaluated retrospectively. Whole brain, subcortical gray matter (GM), cortical GM, and cerebral white matter (WM) volume fractions were obtained. DMTs were categorized as higher (DMT-H: natalizumab and rituximab) or lower (DMT-L: interferon-beta and glatiramer acetate) efficacy. Follow-up epochs were analyzed if participants had been on a DMT for ⩾6 months prior to baseline and had at least one follow-up MRI while on DMTs in the same category., Results: A total of 86 DMT epochs (DMT-H: n  = 32; DMT-L: n  = 54) from 78 participants fulfilled the study inclusion criteria. Mean follow-up was 2.4 years. Annualized rates of thalamic (-0.15% vs -0.81%; p  = 0.001) and putaminal (-0.27% vs -0.73%; p  = 0.001) atrophy were slower during DMT-H compared to DMT-L epochs. These results remained significant in multivariate analyses including demographics, clinical characteristics, and T2 lesion volume., Conclusion: DMT-H treatment may be associated with slower rates of subcortical GM atrophy, especially of the thalamus and putamen. Thalamic and putaminal volumes are promising imaging biomarkers in MS.

The falx cerebri and tentorium cerebelli are dural structures found in the brain. Due to the roles both structures play in constraining brain motion, the falx and tentorium must be identified and included in finite element models of the head to accurately predict brain dynamics during injury events. To date there has been very little research work on automatically segmenting these two structures, which is understandable given that their 1) thin structure challenges the resolution limits of in vivo 3D imaging, and 2) contrast with respect to surrounding tissue is low in standard magnetic resonance imaging. An automatic segmentation algorithm to find the falx and tentorium which uses the results of a multi-atlas segmentation and cortical reconstruction algorithm is proposed. Gray matter labels are used to find the location of the falx and tentorium. The proposed algorithm is applied to five datasets with manual delineations. 3D visualizations of the final results are provided, and Hausdorff distance (HD) and mean surface distance (MSD) is calculated to quantify the accuracy of the proposed method. For the falx, the mean HD is 43.84 voxels and the mean MSD is 2.78 voxels, with the largest errors occurring at the frontal inferior falx boundary. For the tentorium, the mean HD is 14.50 voxels and mean MSD is 1.38 voxels.

Automatic thalamus segmentation is useful to track changes in thalamic volume over time. In this work, we introduce a task-driven dictionary learning framework to find the optimal dictionary given a set of eleven features obtained from T1-weighted MRI and diffusion tensor imaging. In this dictionary learning framework, a linear classifier is designed concurrently to classify voxels as belonging to the thalamus or non-thalamus class. Morphological post-processing is applied to produce the final thalamus segmentation. Due to the uneven size of the training data samples for the non-thalamus and thalamus classes, a non-uniform sampling scheme is proposed to train the classifier to better discriminate between the two classes around the boundary of the thalamus. Experiments are conducted on data collected from 22 subjects with manually delineated ground truth. The experimental results are promising in terms of improvements in the Dice coefficient of the thalamus segmentation over state-of-the-art atlas-based thalamus segmentation algorithms.

Segmentation of the thalamus and thalamic nuclei is useful to quantify volumetric changes from neurodegenerative diseases. Most thalamus segmentation algorithms only use T1-weighted magnetic resonance images and current thalamic parcellation methods require manual interaction. Smaller nuclei, such as the lateral and medial geniculates, are challenging to locate due to their small size. We propose an automated segmentation algorithm using a set of features derived from diffusion tensor image (DTI) and thalamic nuclei location priors. After extracting features, a hierarchical random forest classifier is trained to locate the thalamus. A second random forest classifies thalamus voxels as belonging to one of six thalamic nuclei classes. The proposed algorithm was tested using a leave-one-out cross validation scheme and compared with state-of-the-art algorithms. The proposed algorithm has a higher Dice score compared to other methods for the whole thalamus and several nuclei.

Diffusion MRI (dMRI) provides a noninvasive tool for investigating white matter tracts. Probabilistic fiber tracking has been proposed to represent the fiber structures as 3D streamlines while taking the uncertainty introduced by noise into account. In this paper, we propose a probabilistic fiber tracking method based on bootstrapping a multi-tensor model with a fixed tensor basis. The fiber orientation (FO) estimation is formulated as a Lasso problem. Then by resampling the residuals calculated using a modified Lasso estimator to create synthetic diffusion signals, a distribution of FOs is estimated. Probabilistic fiber tracking can then be performed by sampling from the FO distribution. Experiments were performed on a digital crossing phantom and brain dMRI for validation.

A novel algorithm for correcting illumination variation in dermatological photographs via a multi-stage modeling of the underlying illumination is proposed for the purpose of skin lesion analysis. First, an initial illumination estimate is obtained via a non-parametric modeling strategy based on Monte Carlo sampling. Next, a subset of pixels from the non-parametric estimate is used to determine a parametric estimate of the illumination based on a quadratic surface model. Using the parametric illumination estimate, the reflectance map is obtained and used to correct the photograph. The photographs corrected using the proposed algorithm are compared to uncorrected photographs and to a state-of-the-art correction algorithm. Qualitatively, a visual comparison is performed, while quantitatively, the coefficient of variation of skin pixel intensities is calculated and the precision-recall curve for segmentation of skin lesions is graphed. Results show that the proposed algorithm has a lower coefficient of variation and an improved precision-recall curve.

A promising approach to prostate cancer diagnosis is multi-parametric MRI. One of the key modalities used in multi-parametric MRI is diffusion weighted MRI. Using multiple diffusion weighted MR acquisitions taken with different magnetic gradient strengths, the apparent diffusion coefficient (ADC) is calculated and can be used to identify tumors in the prostate. Current algorithms used to calculate ADC assume a parametric measurement model, but this assumption is not true due to the presence of additional phenomena during the acquisition process. A novel Non-parametric Estimated ADC (NEstA) algorithm is proposed which uses a Monte Carlo strategy to learn the inherent measurement distribution model based on the underlying statistical behavior of the DWI measurements to estimate the ADC values. The proposed algorithm is compared to the results of the commonly used least-squares (LS) estimation algorithm for computing ADC values. Nine test patient cases with visible tumors in the prostate gland were processed using both algorithms and compared visually. It was found that NEstA produced ADC data with reduced artifacts while preserving structure. Quantitatively, Fisher's criterion measuring the separability of the healthy prostate and tumor tissues was computed for the nine patient cases, comparing the NEstA and LS methods. It was found that Fisher's criterion increased with the NEstA method, meaning the separation of classes was more pronounced.

High b-value diffusion-weighted imaging is a promising approach for diagnosing and localizing cancer in the prostate gland. However, ultra-high b-value imaging is difficult to achieve at a high signal-to-noise ratio due to hardware limitations. An alternative approach being recently discussed is computed diffusion-weighted imaging, which allows for estimation of ultra-high b-value images from a set of diffusion-weighted acquisitions with different magnetic gradient strengths. This paper presents a quantitative investigative analysis of the improvement in tumour separability in the prostate gland from using ultra-high b-value computed diffusion-weighted imaging. The analysis computes ultra-high b-value images for six patient cases and investigates the separability of the tumour from the normal prostate gland. Based on quantitative metrics such as expected probability of classification error and the Receiver Operating Characteristic (ROC), it was found that the use of ultra-high computed diffusion-weighted imaging may significantly improve tumour separability, with a b-value around 3000 providing optimal separability.