2,811 research outputs found
Brain Tumor Vascular Network Segmentation from Micro-Tomography
Micro-tomography produces high resolution images of bio- logical structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We deïŹne and use a condi- tional random ïŹeld for segmenting the output of a watershed algorithm. The tumoral and normal classes are thus character- ized by their respective distribution of watershed region size interpreted as local vascular territories
Vascular network segmentation: an unsupervised approach
Micro-tomography produces high resolution images of biological structures such as vascular networks. In this paper, we present a new approach for segmenting vascular network into pathological and normal regions from considering their micro-vessel 3D structure only. We consider a partition of the volume obtained by a watershed algorithm based on the distance from the nearest vessel. Each territory is characterized by its volume and the local vascular density. The volume and density maps are first regularized by minimizing the total variation. Then, a new approach is proposed to segment the volume from the two previous restored images based on hypothesis testing. Results are presented on 3D micro-tomographic images of the brain micro-vascular network
Simultaneous submicrometric 3D imaging of the micro-vascular network and the neuronal system in a mouse spinal cord
Defaults in vascular (VN) and neuronal networks of spinal cord are
responsible for serious neurodegenerative pathologies. Because of inadequate
investigation tools, the lacking knowledge of the complete fine structure of VN
and neuronal systems is a crucial problem. Conventional 2D imaging yields
incomplete spatial coverage leading to possible data misinterpretation, whereas
standard 3D computed tomography imaging achieves insufficient resolution and
contrast. We show that X-ray high-resolution phase-contrast tomography allows
the simultaneous visualization of three-dimensional VN and neuronal systems of
mouse spinal cord at scales spanning from millimeters to hundreds of
nanometers, with neither contrast agent nor a destructive sample-preparation.
We image both the 3D distribution of micro-capillary network and the
micrometric nerve fibers, axon-bundles and neuron soma. Our approach is a
crucial tool for pre-clinical investigation of neurodegenerative pathologies
and spinal-cord-injuries. In particular, it should be an optimal tool to
resolve the entangled relationship between VN and neuronal system.Comment: 15 pages, 6 figure
Whole-brain vasculature reconstruction at the single capillary level
The distinct organization of the brainâs vascular network ensures that it is adequately supplied with oxygen and nutrients. However, despite this fundamental role, a detailed reconstruction of the brain-wide vasculature at the capillary level remains elusive, due to insufficient image quality using the best available techniques. Here, we demonstrate a novel approach that improves vascular demarcation by combining CLARITY with a vascular staining approach that can fill the entire blood vessel lumen and imaging with light-sheet fluorescence microscopy. This method significantly improves image contrast, particularly in depth, thereby allowing reliable application of automatic segmentation algorithms, which play an increasingly important role in high-throughput imaging of the terabyte-sized datasets now routinely produced. Furthermore, our novel method is compatible with endogenous fluorescence, thus allowing simultaneous investigations of vasculature and genetically targeted neurons. We believe our new method will be valuable for future brain-wide investigations of the capillary network
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
From homogeneous to fractal normal and tumorous microvascular networks in the brain
We studied normal and tumorous three-dimensional (3D) microvascular networks in primate and rat
brain. Tissues were prepared following a new preparation technique intended for high-resolution
synchrotron tomography of microvascular networks. The resulting 3D images with a spatial
resolution of less than the minimum capillary diameter permit a complete description of the entire
vascular network for volumes as large as tens of cubic millimeters. The structural properties of the
vascular networks were investigated by several multiscale methods such as fractal and power-
spectrum analysis. These investigations gave a new coherent picture of normal and pathological
complex vascular structures. They showed that normal cortical vascular networks have scale-
invariant fractal properties on a small scale from 1.4 lm up to 40 to 65 lm. Above this threshold,
vascular networks can be considered as homogeneous. Tumor vascular networks show similar
characteristics, but the validity range of the fractal regime extend to much larger spatial dimensions.
These 3D results shed new light on previous two dimensional analyses giving for the first time a
direct measurement of vascular modules associated with vessel-tissue surface exchange
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