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Examining volumetric differences of the amygdala and anterior-posterior regions of the hippocampus is important for understanding cognition and clinical disorders. However, the gold standard manual segmentation of these structures is time and labor-intensive. Automated, accurate, and reproducible techniques to segment the hippocampus and amygdala are desirable. Here, we present a hierarchical approach to multi-atlas segmentation of the hippocampus head, body and tail and the amygdala based on atlases from 195 individuals. The Open Vanderbilt Archive of the temporal Lobe (OVAL) segmentation technique outperforms the commonly used FreeSurfer, FSL FIRST, and whole-brain multi-atlas segmentation approaches for the full hippocampus and amygdala and nears or exceeds inter-rater reproducibility for segmentation of the hippocampus head, body and tail. OVAL has been released in open-source and is freely available. • Present labeling protocols for the hippocampus head, body and amygdala. • created an atlas population of 195 subjects with manually traced hippocampi and automatically segmented amygdalae • presented the OVAL algorithm which is a hierarchical approach for the the full hippocampus and amygdala segmentation [ABSTRACT FROM AUTHOR]

Deep learning has shown remarkable improvements in the analysis of medical images without the need for engineered features. In this work, we hypothesize that deep learning is complementary to traditional feature estimation. We propose a network design to include traditional structural imaging features alongside deep convolutional ones and illustrate this approach on the task of imaging-based age prediction in two separate contexts: T1-weighted brain magnetic resonance imaging (MRI) (N = 5121, ages 4–96, healthy controls) and computed tomography (CT) of the head (N = 1313, ages 1–97, healthy controls). In brain MRI, we can predict age with a mean absolute error of 4.08 years by combining raw images along with engineered structural features, compared to 5.00 years using image-derived features alone and 8.23 years using structural features alone. In head CT, we can predict age with a median absolute error of 9.99 years combining features, compared to 11.02 years with image-derived features alone and 13.28 years with structural features alone. These results show that we can complement traditional feature estimation using deep learning to improve prediction tasks. As the field of medical image processing continues to integrate deep learning, it will be important to use the new techniques to complement traditional imaging features instead of fully displacing them. [ABSTRACT FROM AUTHOR]

The basal ganglia and limbic system, particularly the thalamus, putamen, internal and external globus pallidus, substantia nigra, and sub-thalamic nucleus, comprise a clinically relevant signal network for Parkinson's disease. In order to manually trace these structures, a combination of high-resolution and specialized sequences at 7 T are used, but it is not feasible to routinely scan clinical patients in those scanners. Targeted imaging sequences at 3 T have been presented to enhance contrast in a select group of these structures. In this work, we show that a series of atlases generated at 7 T can be used to accurately segment these structures at 3 T using a combination of standard and optimized imaging sequences, though no one approach provided the best result across all structures. In the thalamus and putamen, a median Dice Similarity Coefficient (DSC) over 0.88 and a mean surface distance <1.0 mm were achieved using a combination of T1 and an optimized inversion recovery imaging sequences. In the internal and external globus pallidus a DSC over 0.75 and a mean surface distance <1.2 mm were achieved using a combination of T1 and inversion recovery imaging sequences. In the substantia nigra and sub-thalamic nucleus a DSC of over 0.6 and a mean surface distance of <1.0 mm were achieved using the inversion recovery imaging sequence. On average, using T1 and optimized inversion recovery together significantly improved segmentation results than over individual modality (p < 0.05 Wilcoxon sign-rank test). [ABSTRACT FROM AUTHOR]

Background: The role of microRNAs (miRNAs), a key class of regulators of mRNA expression and translation, in patients with eosinophilic esophagitis (EoE) has not been explored. Objective: We aimed to identify miRNAs dysregulated in patients with EoE and assess the potential of these miRNAs as disease biomarkers. Methods: Esophageal miRNA expression was profiled in patients with active EoE and those with glucocorticoid-induced disease remission. Expression profiles were compared with those of healthy control subjects and patients with chronic (noneosinophilic) esophagitis. Expression levels of the top differentially expressed miRNAs from the plasma of patients with active EoE and patients with EoE remission were compared with those of healthy control subjects. Results: EoE was associated with 32 differentially regulated miRNAs and was distinguished from noneosinophilic forms of esophagitis. The expression levels of the most upregulated miRNAs (miR-21 and miR-223) and the most downregulated miRNA (miR-375) strongly correlated with esophageal eosinophil levels. Bioinformatic analysis predicted interplay of miR-21 and miR-223 with key roles in the polarization of adaptive immunity and regulation of eosinophilia, and indeed, these miRNAs correlated with key elements of the EoE transcriptome. The differentially expressed miRNAs were largely reversible in patients who responded to glucocorticoid treatment. EoE remission induced a single miRNA (miR-675) likely to be involved in DNA methylation. Plasma analysis of the most upregulated esophageal miRNAs identified miR-146a, miR-146b, and miR-223 as the most differentially expressed miRNAs in the plasma. Conclusions: We have identified a marked dysregulated expression of a select group of miRNAs in patients with EoE and defined their reversibility with glucocorticoid treatment and their potential value as invasive and noninvasive biomarkers. [Copyright &y& Elsevier]