Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and th...
We explore a method for reconstructing visual stimuli from brain activity. Using large databases of natural images we trained a deep convolutional generative adversarial network capable of generating gray scale photos, similar to stimuli presented du...
PURPOSE: High grade gliomas (HGGs) are infiltrative in nature. Differentiation between vasogenic edema and non-contrast enhancing tumor is difficult as both appear hyperintense in T-W/FLAIR images. Most studies involving differentiation between vasog...
International journal of computer assisted radiology and surgery
Jul 19, 2018
PURPOSE: We present a cross-modality and fully automatic pipeline for labeling of intervertebral discs and vertebrae in volumetric data of the lumbar and thoracolumbar spine. The main goal is to provide an algorithm that is applicable to a wide range...
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jul 18, 2018
In this paper, we propose a novel contour-seed pairs learning-based framework for robust and automated cell/nucleus segmentation. Automated granular object segmentation in microscopy images has significant clinical importance for pathology grading of...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jul 17, 2018
In this article, we apply the deep learning technique to medical field for the teeth detection and classification of dental periapical radiographs, which is important for the medical curing and postmortem identification. We detect teeth in an input X...
Attention Deficit Hyperactive Disorder (ADHD) is one of the most common diseases in school aged children. In this paper, we consider using fMRI data with classification techniques to aid the diagnosis of ADHD and propose a bi-objective ADHD classific...
BACKGROUND: Early and automatic detection of pulmonary nodules from CT lung screening is the prerequisite for precise management of lung cancer. However, a large number of false positives appear in order to increase the sensitivity, especially for de...
The objective of this study is to develop a convolutional neural network (CNN) for computed tomography (CT) image super-resolution. The network learns an end-to-end mapping between low (thick-slice thickness) and high (thin-slice thickness) resolutio...
This study aimed at elucidating the relationship between the number of computed tomography (CT) images, including data concerning the accuracy of models and contrast enhancement for classifying the images. We enrolled 1539 patients who underwent cont...
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