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Early and more sensitive detection of small aneurysms in 3D cerebral angiograms is required to prevent potentially fatal rupture events. Herein, we propose a novel method that entails structure enhancement filtering to highlight potential aneurysm locations, intra-vascular distance mapping for regional vascular shape encoding and dimensionality reduction and a convolutional neural network to automatically...
We present a learning based fully automatic method to detect and segment the prostate in T2 weighted MR scans. It consists of a localization stage which uses a learned global context to detect the prostate location. This is followed by a segmentation stage which uses a learned local context using prostatic segment specific discriminative classifiers, to compute the probability of a point being on...
The automated detection of cerebral vessels is of great importance in understanding of the diagnosis, treatment and mechanism of many brain vascular pathologies. However, automatic vessel detection from 3D angiography continues to be an open issue. In this paper we introduce a novel 3D symmetry filter that has excellent performance on enhancing vessels in magnetic resonance angiography (MRA). The...
For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose...
Surgical resection of portions of the temporal lobe is the standard of care for patients with refractory mesial temporal lobe epilepsy. While this reduces seizures, it often results in an inability to form new memories, which leads to difficulties in social situations, learning, and suboptimal quality of life. Learning about the success or failure to form new memory in such patients is critical if...
Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy...
Brain connectivity is increasingly being studied using connectomes. Typical structural connectome definitions do not directly take white matter pathology into account. Presumably, pathology impedes signal transmission along fibres, leading to a reduction in function. In order to directly study disconnection and localize pathology within the connectome, we present the disconnectome, which only considers...
The partial volume effect (PVE) due to the low resolution of SPECT in brain SPECT volumes can be modeled as a convolution of a three-dimensional point-spread function (PSF) with the underlying true radioactivity. In this paper, a deconvolution guided by the edge locations in the geometric transfer matrix (GTM) method as a weighted regularization, denoted as RGTM, was proposed to take into account...
We present a method based on deformable meshes for the reconstruction of the cortical surfaces of the developing human brain at the neonatal period. It employs a brain segmentation for the reconstruction of an initial inner cortical surface mesh. Errors in the segmentation resulting from poor tissue contrast in neonatal MRI and partial volume effects are subsequently accounted for by a local edge-based...
Streaking artifacts caused by metallic objects severely affect the visual quality of CT images, resulting in medical misdiagnosis. Commonly used approaches for metal artifact reduction usually consist of interpolation and iterative methods. The former one tends to lose image quality by introducing extra artifacts, while the latter is more computational expensive. This paper proposes a new approach...
We investigate a novel, parallel implementation of active contours for image segmentation combining a multi-agent system with a quad-edge representation of the contour. The control points of the contour evolve independently from one another in a parallel fashion, handling contour deformation, and convergence, while the quad-edge representation simplifies contour manipulation and local re-sampling...
We propose an automated platform for extra-coronary calcification detection on low dose CT scans. We utilize faster regional convolutional neural networks (R-CNN) to directly detect calcifications at the lesion-level without performing vessel extraction. To segment detected calcifications at the voxel-level, we employ holistically nested edge detection (HED). CT scans of 112 vasculitis patients and...
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