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The design of coded apertures for compressive X-ray tomosynthesis is addressed here, based on the analysis of the coherence of the sensing matrix. It is shown that minimizing the inner products between the columns of the transfer function matrix, it is possible to obtain significant improvements with respect to the results obtained with the use of random codes. The computational cost of optimization...
High radiation dose impedes the development of in-vivo micro-CT. In this paper, we presents a low dose and fast invivo micro computed tomography (micro-CT) system based on equally sloped tomography (EST) technique and the monochromatic synchrotron X-ray source. Comparing with regular CT, the projection number required for our imaging system can be reduced by about 75%. In addition, combining with...
Automatic detection and classification of lesions in medical images is a desirable goal, with numerous clinical applications. In breast imaging, multiple modalities such as X-ray, ultrasound and MRI are often used in the diagnostic workflow. Training robust classifiers for each modality is challenging due to the typically small size of the available datasets. We propose to use cross-modal transfer...
In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning...
Unilateral peripheral facial paralysis (UPFP) is a form of facial nerve paralysis and clinically classified according to facial asymmetry. Prompt and precise assessment is crucial to the neural rehabilitation of UPFP. For UPFP assessment, most of the existing assessment systems are subjective and empirical. Therefore, an objective assessment system will help clinical doctors to obtain a prompt and...
Automatic segmentation of retinal blood vessels from fundus images plays an important role in the computer aided diagnosis of retinal diseases. The task of blood vessel segmentation is challenging due to the extreme variations in morphology of the vessels against noisy background. In this paper, we formulate the segmentation task as a multi-label inference task and utilize the implicit advantages...
Accurate skin lesion segmentation is an important yet challenging problem for medical image analysis. The skin lesion segmentation is subject to variety of challenges such as the significant pattern and colour diversity found within the lesions, presence of various artifacts, etc. In this paper, we present two fully convolutional networks with several side outputs to take advantage of discriminative...
Knowledge transfer impacts the performance of deep learning — the state of the art for image classification tasks, including automated melanoma screening. Deep learning's greed for large amounts of training data poses a challenge for medical tasks, which we can alleviate by recycling knowledge from models trained on different tasks, in a scheme called transfer learning. Although much of the best art...
We present a new surface energy potential for the segmentation of cylindrical objects in 3D medical imaging using parametric spline active contours (a.k.a. spline-snakes). Our energy formulation is based on an optimal steerable surface detector. Thus, we combine the concept of steerability with spline-snakes that have open topology for semi-automatic segmentation. We show that the proposed energy...
Cell detection in microscopy images is a common and challenging task. We propose a new approach for mitotic cell detection in histopathology images, which is based on a Deep Residual Network architecture combined with Hough voting. We propose a voting layer for neural networks and introduce a novel loss function. Our approach is learned from scratch using cell centroids and the original images. We...
Dynamic functional connectivity (dFC) analysis aims at understanding how interactions across the brain resting-state networks (RSNs) evolve over time. Here, we introduce a novel methodological framework operating at the level of RSN activity time courses. Through the use of coupled hidden Markov models (CHMMs), we model cross-network couplings, i.e. the ability of one RSN to influence state transitions...
The structural analysis of nerve fibers of the human brain is an important topic in current neuroscience. To obtain information about neural connections with micrometer resolution, polarized light imaging (3D PLI) of histological brain sections is well suited. In our application, both high-resolution (HR, 64µm in-plane pixel size) and ultra-high resolution (ultra-HR, 1.3µm) 3D PLI data of human brain...
Multisubject analysis helps to jointly analyze themedical data from multiple subjects, to make insightful inferences. Multi set canonical correlation analysis (MCCA), which extends the application of canonical correlation analysis to more than two datasets, is one such statistical technique to perform multisubject analysis. MCCA aims to compute optimal data transformations such that overall correlation...
Resting-state fMRI provides challenging data that needs to be analyzed without knowledge about timing or duration of neuronal events. The “total activation” framework is one recent approach that combines temporal and spatial regularization to deconvolve the fMRI signals; i.e., undo them from the influence of the hemodynamic response. The temporal regularization is using generalized total variation...
This paper addresses the estimation of pairwise supervoxel correspondences toward automatic semi-dense medical image registration. Supervoxel matching is performed through random forests (RF) with supervoxel indexes as label entities to predict matching areas in another target image. Ensuring accurate supervoxel boundary adherence requires a fine supervoxel decomposition which highly increases learning...
Multi-atlas based label fusionmethods have been successfully used for medical image segmentation. In the field of brain region segmentation, multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases. Most of existing multi-atlas based methods usually use intensity feature, which is hard to capture high-order information...
Aortic dissection is a condition in which a tear in the inner wall of the aorta allows blood to flow between two layers of the aortic wall. Aortic dissection is associated with severe chest pain and can be deadly. Contrast-enhanced CT is the main modality for detection of aortic dissection. Aortic dissection is one of the target abnormalities during evaluation of a triple rule-out CT in emergency...
Segmentation of skin lesions is considered as an important step in computer aided diagnosis (CAD) for melanoma diagnosis. There have many attempts to segment skin lesions in a semi- or fully-automated manner. Existing methods, however, have problems with over- or under-segmentation and do not perform well with challenging skin lesions such as when a lesion is partially connected to the background...
Difficulty on collecting annotated medical images leads to lack of enough supervision and makes discrimination tasks challenging. However, raw data, e.g., spatial context information from 3D CT images, even without annotation, may contain rich useful information. In this paper, we exploit spatial context information as a source of supervision to solve discrimination tasks for fine-grained body part...
We report an automated method for characterization of microvessel morphology in micrographs of brain tissue sections to enable the facile, quantitative analysis of vascular differences across large datasets consisting of hundreds of images with thousands of blood vessel objects. Our objective is to show that virtual 3D parametric models of vasculature are adequately capable of representing the morphology...
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