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We present a 3D variational optical flow method for fluorescence image sequences which preserves discontinuities in the computed flow field. We propose to minimize an energy function composed of a…
Providing closed and well-connected boundaries of coronary artery is essential to assist cardiologists in the diagnosis of coronary artery disease (CAD). Recently, several deep learning-based methods…
The early detection of dental plaque could prevent periodontal diseases and dental caries, however, it is difficult to recognize it without the use of medical dyeing reagent due to the low contrast…
Accurate interpretation and analysis of echocardiography is important in assessing cardiovascular health. However, motion tracking often relies on accurate segmentation of the myocardium, which can…
Speed of sound (SOS) is a biomarker that aides clinicians in tracking the onset and progression of diseases such as breast cancer and fatty liver disease. In this paper, we propose a framework to…
Semantic segmentation is an essential step for electron microscopy (EM) image analysis. Although supervised models have achieved significant progress, the need for labor intensive pixel-wise…
Recent advances in supervised deep learning, mainly using convolutional neural networks, enabled the fast acquisition of high-quality brain tissue segmentation from structural magnetic resonance…
The recently proposed frequency-domain technique for photoacoustic (PA) image formation helps to differentiate between different-sized structures. Although this technique has provided encouraging…
New machine learning models designed to capture the histopathology of tissues should account not only for the phenotype and morphology of the cells, but also learn complex spatial relationships…
Due to the poor image information of lymphoma in PET images, it is still a challenge to segment them correctly. In this work, a fusion strategy by 2D multi-view and 3D networks is proposed to take…
Autofocus (AF) methods are extensively used in biomicroscopy, for example to acquire timelapses, where the imaged objects tend to drift out of focus. AF algorithms determine an optimal distance by…
A Native of Belgrade and an IEEE Life fellow, Kokotovic studied widely in both Eastern and Western Europe before joining the faculty at the University of Illinois where in the 1960s he developed…
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In this paper, we propose a deep learning approach for the segmentation of body parts in computer tomography (CT) localizer images. Such images pose difficulties in the automatic image analysis on…
Magnetic resonance imaging (MRI) acquisition is an inherently slow process whose acceleration has been the subject of much investigation. In recent years, the explosive advance of deep learning…
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The choroid plexus is the primary organ that secretes the cerebrospinal fluid. Its structure and function may be associated with the brain drainage pathway and the clearance of amyloid-beta in…
In this paper, we propose a framework for accelerated reconstruction of 2D phase contrast magnetic resonance images from undersampled k-space domain by using deep learning methods. Undersampling in k…
McCarthy, a long time professor of computer science at Stanford was a the founders of the field of artificial intelligence, and applied artificial intelligence to robotic arms. He also devised the…
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Digital breast tomosynthesis images provide volumetric morphological information of the breast helping physicians to detect malign lesions. In this work, we propose a new spatially adaptive total…
We propose a model-based deep learning architecture for the reconstruction of highly accelerated diffusion magnetic resonance imaging (MRI) that enables high-resolution imaging. The proposed…
Identification of the specific brain networks that are vulnerable or resilient in neurodegenerative diseases can help to better understand the disease effects and derive new connectomic imaging…
Suppression of bony structures in chest radiographs is essential for many computer-aided diagnosis tasks. In this paper, we propose a Disentanglement AutoEncoder (DAE) for bone suppression. As the…
Mechanical properties of tissue provide valuable information for identifying lesions. One approach to obtain quantitative estimates of elastic properties is shear wave elastography with optical…
The analysis of the brain surface modeled as a graph mesh is a challenging task. Conventional deep learning approaches often rely on data lying in the Euclidean space. As an extension to irregular…
Accelerating data acquisition in magnetic resonance imaging (MRI) has been of perennial interest due to its prohibitively slow data acquisition process. Recent trends in accelerating MRI employ data-…