2,012 research outputs found
A novel MRA-based framework for the detection of changes in cerebrovascular blood pressure.
Background: High blood pressure (HBP) affects 75 million adults and is the primary or contributing cause of mortality in 410,000 adults each year in the United States. Chronic HBP leads to cerebrovascular changes and is a significant contributor for strokes, dementia, and cognitive impairment. Non-invasive measurement of changes in cerebral vasculature and blood pressure (BP) may enable physicians to optimally treat HBP patients. This manuscript describes a method to non-invasively quantify changes in cerebral vasculature and BP using Magnetic Resonance Angiography (MRA) imaging.
Methods: MRA images and BP measurements were obtained from patients (n=15, M=8, F=7, Age= 49.2 ± 7.3 years) over a span of 700 days. A novel segmentation algorithm was developed to identify brain vasculature from surrounding tissue. The data was processed to calculate the vascular probability distribution function (PDF); a measure of the vascular diameters in the brain. The initial (day 0) PDF and final (day 700) PDF were used to correlate the changes in cerebral vasculature and BP. Correlation was determined by a mixed effects linear model analysis.
Results: The segmentation algorithm had a 99.9% specificity and 99.7% sensitivity in identifying and delineating cerebral vasculature. The PDFs had a statistically significant correlation to BP changes below the circle of Willis (p-value = 0.0007), but not significant (p-value = 0.53) above the circle of Willis, due to smaller blood vessels.
Conclusion: Changes in cerebral vasculature and pressure can be non-invasively obtained through MRA image analysis, which may be a useful tool for clinicians to optimize medical management of HBP
Segmenting the left ventricle in 3D using a coupled ASM and a learned non-rigid spatial model
This paper presents a new approach to higher dimensional segmentation. We present an extended Active Shape Model (ASM) formulation for the segmentation of multi-contour anatomical structures. We employ coupling and weighting schemes to improve the robustness of ASM segmentation. 3D segmentation is achieved through propagation of a 2D ASM using a learned non-rigid spatial model. This approach does not suffer from the training and aligning difficulties faced by direct 3D model-based methods used today. Experimental results are encouraging at this early stage, and future directions of research are provided
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
Segmentation of image ensembles via latent atlases
Spatial priors, such as probabilistic atlases, play an important role in MRI segmentation. However, the availability of comprehensive, reliable and suitable manual segmentations for atlas construction is limited. We therefore propose a method for joint segmentation of corresponding regions of interest in a collection of aligned images that does not require labeled training data. Instead, a latent atlas, initialized by at most a single manual segmentation, is inferred from the evolving segmentations of the ensemble. The algorithm is based on probabilistic principles but is solved using partial differential equations (PDEs) and energy minimization criteria. We evaluate the method on two datasets, segmenting subcortical and cortical structures in a multi-subject study and extracting brain tumors in a single-subject multi-modal longitudinal experiment. We compare the segmentation results to manual segmentations, when those exist, and to the results of a state-of-the-art atlas-based segmentation method. The quality of the results supports the latent atlas as a promising alternative when existing atlases are not compatible with the images to be segmented.National Institutes of Health (U.S.) (National Institute for Biomedical Imaging and Bioengineering (U.S.)/National Alliance for Medical Image Computing (U.S.) U54-EB005149)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Neuroimaging Analysis Center (U.S.) P41-RR13218)National Institutes of Health (U.S.) (National Institute of Neurological Disorders and Stroke (U.S.) R01-NS051826)National Institutes of Health (U.S.) (National Center for Research Resources (U.S.)/Biomedical Informatics Research Network U24-RR021382)National Science Foundation (U.S.) (CAREER Award 0642971)German Academy of Sciences Leopoldina (Fellowship LPDS 2009-10)Academy of Finland (Grant 133611
Convolutional 3D to 2D Patch Conversion for Pixel-wise Glioma Segmentation in MRI Scans
Structural magnetic resonance imaging (MRI) has been widely utilized for
analysis and diagnosis of brain diseases. Automatic segmentation of brain
tumors is a challenging task for computer-aided diagnosis due to low-tissue
contrast in the tumor subregions. To overcome this, we devise a novel
pixel-wise segmentation framework through a convolutional 3D to 2D MR patch
conversion model to predict class labels of the central pixel in the input
sliding patches. Precisely, we first extract 3D patches from each modality to
calibrate slices through the squeeze and excitation (SE) block. Then, the
output of the SE block is fed directly into subsequent bottleneck layers to
reduce the number of channels. Finally, the calibrated 2D slices are
concatenated to obtain multimodal features through a 2D convolutional neural
network (CNN) for prediction of the central pixel. In our architecture, both
local inter-slice and global intra-slice features are jointly exploited to
predict class label of the central voxel in a given patch through the 2D CNN
classifier. We implicitly apply all modalities through trainable parameters to
assign weights to the contributions of each sequence for segmentation.
Experimental results on the segmentation of brain tumors in multimodal MRI
scans (BraTS'19) demonstrate that our proposed method can efficiently segment
the tumor regions
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