Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments ($>75\mu m$) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models.Comment: 34 pages, 9 figure

Brain capillary networks across species : a few simple organizational requirements are sufficient to reproduce both structure and function

Despite the key role of the capillaries in neurovascular function, a thorough characterization of cerebral capillary network properties is currently lacking. Here, we define a range of metrics (geometrical, topological, flow, mass transfer, and robustness) for quantification of structural differences between brain areas, organs, species, or patient populations and, in parallel, digitally generate synthetic networks that replicate the key organizational features of anatomical networks (isotropy, connectedness, space-filling nature, convexity of tissue domains, characteristic size). To reach these objectives, we first construct a database of the defined metrics for healthy capillary networks obtained from imaging of mouse and human brains. Results show that anatomical networks are topologically equivalent between the two species and that geometrical metrics only differ in scaling. Based on these results, we then devise a method which employs constrained Voronoi diagrams to generate 3D model synthetic cerebral capillary networks that are locally randomized but homogeneous at the network-scale. With appropriate choice of scaling, these networks have equivalent properties to the anatomical data, demonstrated by comparison of the defined metrics. The ability to synthetically replicate cerebral capillary networks opens a broad range of applications, ranging from systematic computational studies of structure-function relationships in healthy capillary networks to detailed analysis of pathological structural degeneration, or even to the development of templates for fabrication of 3D biomimetic vascular networks embedded in tissue-engineered constructs

Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in Alzheimer’s disease mouse models.

Cerebral blood flow (CBF) reductions in Alzheimer’s disease patients and related mouse models have been recognized for decades, but the underlying mechanisms and resulting consequences for Alzheimer’s disease pathogenesis remain poorly understood. In APP/PS1 and 5xFAD mice we found that an increased number of cortical capillaries had stalled blood flow as compared to in wild-type animals, largely due to neutrophils that had adhered in capillary segments and blocked blood flow. Administration of antibodies against the neutrophil marker Ly6G reduced the number of stalled capillaries, leading to both an immediate increase in CBF and rapidly improved performance in spatial and working memory tasks. This study identified a previously uncharacterized cellular mechanism that explains the majority of the CBF reduction seen in two mouse models of Alzheimer’s disease and demonstrated that improving CBF rapidly enhanced short-term memory function. Restoring cerebral perfusion by preventing neutrophil adhesion may provide a strategy for improving cognition in Alzheimer’s disease patients

Virtual Microstructure Generation of Asphaltic Mixtures

This thesis describes the development and application of a virtual microstructure generator incorporated with post-processing image analysis methods that can be used to fabricate a virtual, two-dimensional microstructure of asphaltic mixtures. In the generator, geometrical characteristics such as aggregate gradation, aggregate area fraction, angularity, orientation, and elongation were used to transform data from a three-dimensional (3D) mixture into its two-dimensional (2D) microstructure. The 2D virtual microstructures were generated from real 3D mixture information of asphaltic composites. Resulting virtual microstructures were then compared to real cross-sectional microstructure images obtained from actual samples for validation. Comparison presented a good agreement between the virtual and real microstructures, which demonstrates that the new 3D-2D transformation algorithms were properly developed and implemented into the virtual microstructure generator. Although much future work is required, the current development is at least sufficient to demonstrate the benefits and potential of this effort. Virtual fabrication and testing can result in significant time and cost savings compared to more expensive and repetitive laboratory fabrication and performance tests of actual specimens. Adviser: Yong-Rak Ki

QUANTITATIVE ASSESSMENT OF CEREBRAL MICROVASCULATURE USING MACHINE LEARNING AND NETWORK ANALYSIS

Vasculature networks are responsible for providing reliable blood perfusion to tissues in health or disease conditions. Volumetric imaging approaches, such as multiphoton microscopy, can generate detailed 3D images of blood vessel networks allowing researchers to investigate different aspects of vascular structures and networks in normal physiology and disease mechanisms. Image processing tasks such as vessel segmentation and centerline extraction impede research progress and have prevented the systematic comparison of 3D vascular architecture across large experimental populations in an objective fashion. The work presented in this dissertation provides complete a fully-automated, open-source, and fast image processing pipeline that is transferable to other research areas and practices with minimal interventions and fine-tuning. As a proof of concept, the applications of the proposed pipeline are presented in the contexts of different biomedical and biological research questions ranging from the stalling capillary phenomenon in Alzheimer’s disease to the drought resistance of xylem networks in various tree species and wood types

A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology

The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model's segmentation results mimic manual ground truth. Our code and model are open source at https://github.com/mhaft/DeepVess.National Institute of Biomedical Imaging and Bioengineering (Grant P41EB-015903)National Institutes of Health (U.S.) (Grant P41EB015902

Increasing cerebral blood flow improves cognition into late stages in Alzheimer's disease mice

Alzheimer's disease is associated with a 20-30% reduction in cerebral blood flow. In the APP/PS1 mouse model of Alzheimer's disease, inhibiting neutrophil adhesion using an antibody against the neutrophil specific protein Ly6G was recently shown to drive rapid improvements in cerebral blood flow that was accompanied by an improvement in performance on short-term memory tasks. Here, in a longitudinal aging study, we assessed how far into disease development a single injection of anti-Ly6G treatment can acutely improve short-term memory function. We found that APP/PS1 mice as old as 15-16 months had improved performance on the object replacement and Y-maze tests of spatial and working short-term memory, measured at one day after anti-Ly6G treatment. APP/PS1 mice at 17-18 months of age or older did not show acute improvements in cognitive performance, although we did find that capillary stalls were still reduced and cerebral blood flow was still increased by 17% in 21-22-months-old APP/PS1 mice given anti-Ly6G antibody. These data add to the growing body of evidence suggesting that cerebral blood flow reductions are an important contributing factor to the cognitive dysfunction associated with neurodegenerative disease. Thus, interfering with neutrophil adhesion could be a new therapeutic approach for Alzheimer's disease

Data from: Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models

The health and function of tissue rely on its vasculature network to provide reliable blood perfusion. Volumetric imaging approaches, such as multiphoton microscopy, are able to generate detailed 3D images of blood vessels that could contribute to our understanding of the role of vascular structure in normal physiology and in disease mechanisms. The segmentation of vessels, a core image analysis problem, is a bottleneck that has prevented the systematic comparison of 3D vascular architecture across experimental populations. We explored the use of convolutional neural networks to segment 3D vessels within volumetric in vivo images acquired by multiphoton microscopy. We evaluated different network architectures and machine learning techniques in the context of this segmentation problem. We show that our optimized convolutional neural network architecture, which we call DeepVess, yielded a segmentation accuracy that was better than both the current state-of-the-art and a trained human annotator, while also being orders of magnitude faster. To explore the effects of aging and Alzheimer's disease on capillaries, we applied DeepVess to 3D images of cortical blood vessels in young and old mouse models of Alzheimer's disease and wild type littermates. We found little difference in the distribution of capillary diameter or tortuosity between these groups, but did note a decrease in the number of longer capillary segments (>75μm) in aged animals as compared to young, in both wild type and Alzheimer's disease mouse models. These data support these findings.This work was supported by the European Research Council grant 615102 (NN), the National Institutes of Health grant AG049952 (CS), the National Institutes of Health grants R01LM012719 and R01AG053949 (MS), and the National Science Foundation Cornell NeuroNex Hub grant (1707312, MS and CS)

A pilot study investigating the effects of voluntary exercise on capillary stalling and cerebral blood flow in the APP/PS1 mouse model of Alzheimer's disease.

Exercise exerts a beneficial effect on the major pathological and clinical symptoms associated with Alzheimer's disease in humans and mouse models of the disease. While numerous mechanisms for such benefits from exercise have been proposed, a clear understanding of the causal links remains elusive. Recent studies also suggest that cerebral blood flow in the brain of both Alzheimer's patients and mouse models of the disease is decreased and that the cognitive symptoms can be improved when blood flow is restored. We therefore hypothesized that the mitigating effect of exercise on the development and progression of Alzheimer's disease may be mediated through an increase in the otherwise reduced brain blood flow. To test this idea, we performed a pilot study to examine the impact of three months of voluntary wheel running in a small cohort of ~1-year-old APP/PS1 mice on short-term memory function, brain inflammation, amyloid deposition, and baseline cerebral blood flow. Our findings that exercise led to a trend toward improved spatial short-term memory, reduced brain inflammation, markedly increased neurogenesis in the dentate gyrus, and a reduction in hippocampal amyloid-beta deposits are consistent with other reports on the impact of exercise on the progression of Alzheimer's related symptoms in mouse models. Notably, we did not observe any impact of wheel running on overall baseline blood flow nor on the incidence of non-flowing capillaries, a mechanism we recently identified as one contributing factor to cerebral blood flow deficits in mouse models of Alzheimer's disease. Overall, our findings add to the emerging picture of differential effects of exercise on cognition and blood flow in Alzheimer's disease pathology by showing that capillary stalling is not decreased following exercise