Silver Standard Masks for Data Augmentation Applied to Deep-Learning-Based Skull-Stripping

The bottleneck of convolutional neural networks (CNN) for medical imaging is the number of annotated data required for training. Manual segmentation is considered to be the "gold-standard". However, medical imaging datasets with expert manual segmentation are scarce as this step is time-consuming and expensive. We propose in this work the use of what we refer to as silver standard masks for data augmentation in deep-learning-based skull-stripping also known as brain extraction. We generated the silver standard masks using the consensus algorithm Simultaneous Truth and Performance Level Estimation (STAPLE). We evaluated CNN models generated by the silver and gold standard masks. Then, we validated the silver standard masks for CNNs training in one dataset, and showed its generalization to two other datasets. Our results indicated that models generated with silver standard masks are comparable to models generated with gold standard masks and have better generalizability. Moreover, our results also indicate that silver standard masks could be used to augment the input dataset at training stage, reducing the need for manual segmentation at this step

Semiautomated Multimodal Breast Image Registration

Consideration of information from multiple modalities has been shown to have increased diagnostic power in breast imaging. As a result, new techniques such as microwave imaging continue to be developed. Interpreting these novel image modalities is a challenge, requiring comparison to established techniques such as the gold standard X-ray mammography. However, due to the highly deformable nature of breast tissues, comparison of 3D and 2D modalities is a challenge. To enable this comparison, a registration technique was developed to map features from 2D mammograms to locations in the 3D image space. This technique was developed and tested using magnetic resonance (MR) images as a reference 3D modality, as MR breast imaging is an established technique in clinical practice. The algorithm was validated using a numerical phantom then successfully tested on twenty-four image pairs. Dice's coefficient was used to measure the external goodness of fit, resulting in an excellent overall average of 0.94. Internal agreement was evaluated by examining internal features in consultation with a radiologist, and subjective assessment concludes that reasonable alignment was achieved

Developments in Time-Frequency Analysis of Biomedical Signals and Images Using a Generalized Fourier Synthesis

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Brain iron content in cerebral amyloid angiopathy using quantitative susceptibility mapping

IntroductionCerebral amyloid angiopathy (CAA) is a small vessel disease that causes covert and symptomatic brain hemorrhaging. We hypothesized that persons with CAA would have increased brain iron content detectable by quantitative susceptibility mapping (QSM) on magnetic resonance imaging (MRI), and that higher iron content would be associated with worse cognition.MethodsParticipants with CAA (n = 21), mild Alzheimer’s disease with dementia (AD-dementia; n = 14), and normal controls (NC; n = 83) underwent 3T MRI. Post-processing QSM techniques were applied to obtain susceptibility values for regions of the frontal and occipital lobe, thalamus, caudate, putamen, pallidum, and hippocampus. Linear regression was used to examine differences between groups, and associations with global cognition, controlling for multiple comparisons using the false discovery rate method.ResultsNo differences were found between regions of interest in CAA compared to NC. In AD, the calcarine sulcus had greater iron than NC (β = 0.99 [95% CI: 0.44, 1.53], q < 0.01). However, calcarine sulcus iron content was not associated with global cognition, measured by the Montreal Cognitive Assessment (p > 0.05 for all participants, NC, CAA, and AD).DiscussionAfter correcting for multiple comparisons, brain iron content, measured via QSM, was not elevated in CAA compared to NC in this exploratory study

Longitudinal decrease in blood oxygenation level dependent response in cerebral amyloid angiopathy

AbstractLower blood oxygenation level dependent (BOLD) signal changes in response to a visual stimulus in functional magnetic resonance imaging (fMRI) have been observed in cross-sectional studies of cerebral amyloid angiopathy (CAA), and are presumed to reflect impaired vascular reactivity. We used fMRI to detect a longitudinal change in BOLD responses to a visual stimulus in CAA, and to determine any correlations between these changes and other established biomarkers of CAA progression. Data were acquired from 22 patients diagnosed with probable CAA (using the Boston Criteria) and 16 healthy controls at baseline and one year. BOLD data were generated from the 200 most active voxels of the primary visual cortex during the fMRI visual stimulus (passively viewing an alternating checkerboard pattern). In general, BOLD amplitudes were lower at one year compared to baseline in patients with CAA (p=0.01) but were unchanged in controls (p=0.18). The longitudinal difference in BOLD amplitudes was significantly lower in CAA compared to controls (p<0.001). White matter hyperintensity (WMH) volumes and number of cerebral microbleeds, both presumed to reflect CAA-mediated vascular injury, increased over time in CAA (p=0.007 and p=0.001, respectively). Longitudinal increases in WMH (rs=0.04, p=0.86) or cerebral microbleeds (rs=−0.18, p=0.45) were not associated with the longitudinal decrease in BOLD amplitudes