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The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties (small size, location, and poor visibility on traditional MRI scans). Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. For this work, both OBs were manually annotated in a total of 620 T2w images for training (n=357) and testing. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study (Dice Score (Dice): 0.852, Volume Similarity (VS): 0.910, and Average Hausdorff Distance (AVD): 0.215 mm). Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7 mm HCP resolution (Dice: 0.738, VS: 0.790, and AVD: 0.340 mm), and the default 0.8 mm pipeline resolution (Dice: 0.782, VS: 0.858, and AVD: 0.268 mm). We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects (β=-0.232, p<.01). Furthermore, our method can analyze a 3D volume in less than a minute (GPU) in an end-to-end fashion, providing a validated, efficient, and scalable solution for automatically assessing OB volumes., (Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.)

White matter hyperintensity (WMH) is a common finding in aging population and considered to be a contributor to cognitive decline. Our study aimed to characterize the spatial patterns of WMH in different severities and explore its impact on cognition and brain microstructure in non-demented elderly. Lesions were both qualitatively (Fazekas scale) and quantitatively assessed among 321 community-dwelled individuals with MRI scanning. Voxel- and atlas-based analyses of the whole-brain white matter microstructure were performed. The WMH of the same severities was found to occur uniformly with a specific pattern of lesions. The severity of WMH had a significant negative association with the performance of working and episodic memory, beginning to appear in Fazekas 3 and 4. The white matter tracts presented significant impairments in Fazekas 3, which showed brain-wide changes above Fazekas 4. Lower FA in the superior cerebellar peduncle and left posterior thalamic radiation was mainly associated with episodic memory, and the middle cerebellar peduncle was significantly associated with working memory. These results support that memory is the primary domain to be affected by WMH, and the effect may potentially be influenced by tract-specific WM abnormalities. Fazekas scale 3 might be the critical stage predicting a future decline in cognition.