John P. Kaufhold

Correlations of Neuronal and Microvascular Densities in Murine Cortex Revealed by Direct Counting and Colocalization of Nuclei and Vessels

It is well known that the density of neurons varies within the adult brain. In neocortex, this includes variations in neuronal density between different lamina as well as between different regions. Yet the concomitant variation of the microvessels is largely uncharted. Here, we present automated histological, imaging, and analysis tools to simultaneously map the locations of all neuronal and non-neuronal nuclei and the centerlines and diameters of all blood vessels within thick slabs of neocortex from mice. Based on total inventory measurements of different cortical regions (∼107 cells vectorized across brains), these methods revealed: (1) In three dimensions, the mean distance of the center of neuronal somata to the closest microvessel was 15 μm. (2) Volume samples within lamina of a given region show that the density of microvessels does not match the strong laminar variation in neuronal density. This holds for both agranular and granular cortex. (3) Volume samples in successive radii from the midline to the ventral-lateral edge, where each volume summed the number of cells and microvessels from the pia to the white matter, show a significant correlation between neuronal and microvessel densities. These data show that while neuronal and vascular densities do not track each other on the 100 μm scale of cortical lamina, they do track each other on the 1–10 mm scale of the cortical mantle. The absence of a disproportionate density of blood vessels in granular lamina is argued to be consistent with the initial locus of functional brain imaging signals.

The cortical angiome: an interconnected vascular network with noncolumnar patterns of blood flow

What is the nature of the vascular architecture in the cortex that allows the brain to meet the energy demands of neuronal computations? We used high-throughput histology to reconstruct the complete angioarchitecture and the positions of all neuronal somata of multiple cubic millimeter regions of vibrissa primary sensory cortex in mouse. Vascular networks were derived from the reconstruction. In contrast with the standard model of cortical columns that are tightly linked with the vascular network, graph-theoretical analyses revealed that the subsurface microvasculature formed interconnected loops with a topology that was invariant to the position and boundary of columns. Furthermore, the calculated patterns of blood flow in the networks were unrelated to location of columns. Rather, blood sourced by penetrating arterioles was effectively drained by the penetrating venules to limit lateral perfusion. This analysis provides the underpinning to understand functional imaging and the effect of penetrating vessels strokes on brain viability.

A unified framework for tracking through occlusions and across sensor gaps

A common difficulty encountered in tracking applications is how to track an object that becomes totally occluded, possibly for a significant period of time. Another problem is how to associate objects, or tracklets, across non-overlapping cameras, or between observations of a moving sensor that switches fields of regard. A third problem is how to update appearance models for tracked objects over time. As opposed to using a comprehensive multi-object tracker that must simultaneously deal with these tracking challenges, we present a novel, modular framework that handles each of these problems in a unified manner by the initialization, tracking, and linking of high-confidence tracklets. In this track/suspend/match paradigm, we first analyze the scene to identify areas where tracked objects are likely to become occluded. Tracking is then suspended on occluded objects and re-initiated when they emerge from behind the occlusion. We then associate, or match, suspended tracklets with the new tracklets using full kinematic models for object motion and Gibbsian distributions for object appearance in order to complete the track through the occlusion. Sensor gaps are handled in a similar manner, where tracking is suspended when the sensor looks away and then re-initiated when the sensor returns. Changes in object appearance and orientation during tracking are also seamlessly handled in this framework. Tracklets with low lock scores are terminated. Tracking then resumes on untracked movers with corresponding updated appearance models. These new tracklets are then linked back to the terminated ones as appropriate. Fully automatic tracking results from a moving sensor are presented.

Large-Scale Automated Histology in the Pursuit of Connectomes

How does the brain compute? Answering this question necessitates neuronal connectomes, annotated graphs of all synaptic connections within defined brain areas. Further, understanding the energetics of the brains computations requires vascular graphs. The assembly of a connectome requires sensitive hardware tools to measure neuronal and neurovascular features in all three dimensions, as well as software and machine learning for data analysis and visualization. We present the state of the art on the reconstruction of circuits and vasculature that link brain anatomy and function. Analysis at the scale of tens of nanometers yields connections between identified neurons, while analysis at the micrometer scale yields probabilistic rules of connection between neurons and exact vascular connectivity.

Learning to segment images using region-based perceptual features

The recent establishment of a large-scale ground-truth database of image segmentations [D. Martin et al., 2001] has enabled the development of learning approaches to the general segmentation problem. Using this database, we present an algorithm that learns how to segment images using region-based, perceptual features. The image is first densely segmented into regions and the edges between them using a variant of the Mumford-Shah functional. Each edge is classified as a boundary or non-boundary using a classifier trained on the ground-truth, resulting in an edge image estimating human-designated boundaries. This novel approach has a few distinct advantages over filter-based methods such as local gradient operators. First, the same perceptual features can represent texture as well as regular structure. Second, the features can measure relationships between image elements at arbitrary distances in the image, enabling the detection of Gestalt properties at any scale. Third, texture boundaries can be precisely localized, which is difficult when using filter banks. Finally, the learning system outputs a relatively small set of intuitive perceptual rules for detecting boundaries. The classifier is trained on 200 images in the ground-truth database, and tested on another 100 images according to the benchmark evaluation methods. Edge classification improves the benchmark F-score from 0.54, for the initial Mumford-Shah-variant segmentation, to 0.61 on grayscale images. This increase of 13% demonstrates the versatility and representational power of our perceptual features, as the score exceeds published results for any algorithm restricted to one type of image feature such as texture or brightness gradient.

Micro-calcification detection in digital tomosynthesis mammography

A novel technique for the detection and enhancement of microcalcifications in digital tomosynthesis mammography (DTM) is presented. In this method, the DTM projection images are used directly, instead of using a 3D reconstruction. Calcification residual images are computed for each of the projection images. Calcification detection is then performed over 3D space, based on the values of the calcification residual images at projection points for each 3D point under test. The quantum, electronic, and tissue noise variance at each pixel in each of the calcification residuals is incorporated into the detection algorithm. The 3D calcification detection algorithm finds a minimum variance estimate of calcification attenuation present in 3D space based on the signal and variance of the calcification residual images at the corresponding points in the projection images. The method effectively detects calcifications in 3D in a way that both ameliorates the difficulties of joint tissue/microcalcification tomosynthetic reconstruction (streak artifacts, etc.) and exploits the well understood image properties of microcalcifications as they appear in 2D mammograms. In this method, 3D reconstruction and calcification detection and enhancement are effectively combined to create a calcification detection specific reconstruction. Motivation and details of the technique and statistical results for DTM data are provided.

Recognition and Segmentation of Scene Content using Region-Based Classification

We present a novel method for joint segmentation and pixelwise classification of images, classifying each pixel in the image into one of a set of broad categories. We propose a 2-step approach for this problem, first estimating image structure through dense region segmentation, which provides initial spatial grouping (superpixels), then performing recognition by classifying each superpixel according to its features. Two types of region features are investigated: perceptual grouping features derived from neighborhood relations in the superpixel graph, and a histogram of pixel textons within the superpixel. Region classification is performed by boosting for perceptual features and histogram matching for texton features. We also introduce a novel extension of multi-class boosting: MAP estimation in the space of classifier ensemble outputs. Extensive results on aerial imagery are presented using a label vocabulary of trees, roads, vehicles, grass, shadows, and buildings. We evaluate the two methods across the categories, and compare them to the standard approach of classifying image blocks without prior segmentation. In our experiments perceptual features using multi-class boosting provide the best performance

Topology, dynamics, and control in cortical blood flow elucidated with optical techniques

Blood is a limited resource that supplies neurons and glia with nutriments. How is blood distributed both in a fail-safe way and in response to changing metabolic loads? We examine this issue in rodent cortex. First, in terms of topology, all-optical histology is used to reconstruct the vasculature network. Second, in terms of dynamics, the flux of blood cells in individual vessels is measured in response to neuronal activation and/or the perturbation of flow of a targeted vessel via plasma-mediated ablation. Lastly, in terms of control, optical indicators of neurovascular signaling molecules that alter vessel diameter are measured.