AIMC Topic: Image Processing, Computer-Assisted

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Image-based surrogate biomarkers for molecular subtypes of colorectal cancer.

Bioinformatics (Oxford, England)
MOTIVATION: Whole genome expression profiling of large cohorts of different types of cancer led to the identification of distinct molecular subcategories (subtypes) that may partially explain the observed inter-tumoral heterogeneity. This is also the...

Deep learning for polyp recognition in wireless capsule endoscopy images.

Medical physics
PURPOSE: Wireless capsule endoscopy (WCE) enables physicians to examine the digestive tract without any surgical operations, at the cost of a large volume of images to be analyzed. In the computer-aided diagnosis of WCE images, the main challenge ari...

Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks.

Medical physics
PURPOSE: Accurate segmentation of organs-at-risks (OARs) is the key step for efficient planning of radiation therapy for head and neck (HaN) cancer treatment. In the work, we proposed the first deep learning-based algorithm, for segmentation of OARs ...

Using deep learning to segment breast and fibroglandular tissue in MRI volumes.

Medical physics
PURPOSE: Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surfac...

Right putamen and age are the most discriminant features to diagnose Parkinson's disease by using I-FP-CIT brain SPET data by using an artificial neural network classifier, a classification tree (ClT).

Hellenic journal of nuclear medicine
OBJECTIVE: The differential diagnosis of Parkinson's disease (PD) and other conditions, such as essential tremor and drug-induced parkinsonian syndrome or normal aging brain, represents a diagnostic challenge. I-FP-CIT brain SPET is able to contribut...

Deep Learning: A Primer for Radiologists.

Radiographics : a review publication of the Radiological Society of North America, Inc
Deep learning is a class of machine learning methods that are gaining success and attracting interest in many domains, including computer vision, speech recognition, natural language processing, and playing games. Deep learning methods produce a mapp...

Enhancement of digital radiography image quality using a convolutional neural network.

Journal of X-ray science and technology
Digital radiography system is widely used for noninvasive security check and medical imaging examination. However, the system has a limitation of lower image quality in spatial resolution and signal to noise ratio. In this study, we explored whether ...

Artificial intelligence in mitral valve analysis.

Annals of cardiac anaesthesia
BACKGROUND: Echocardiographic analysis of mitral valve (MV) has become essential for diagnosis and management of patients with MV disease. Currently, the various software used for MV analysis require manual input and are prone to interobserver variab...

Learning-based deformable image registration for infant MR images in the first year of life.

Medical physics
PURPOSE: Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for differ...

Cytopathological image analysis using deep-learning networks in microfluidic microscopy.

Journal of the Optical Society of America. A, Optics, image science, and vision
Cytopathologic testing is one of the most critical steps in the diagnosis of diseases, including cancer. However, the task is laborious and demands skill. Associated high cost and low throughput drew considerable interest in automating the testing pr...