AI Medical Compendium Topic:
Radiographic Image Interpretation, Computer-Assisted

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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.

Artificial intelligence in medicine
This paper presents a novel, fully automatic approach based on a fully convolutional network (FCN) for segmenting liver tumors from CT images. Specifically, we designed a multi-channel fully convolutional network (MC-FCN) to segment liver tumors from...

Early prediction of radiotherapy-induced parotid shrinkage and toxicity based on CT radiomics and fuzzy classification.

Artificial intelligence in medicine
MOTIVATION: Patients under radiotherapy for head-and-neck cancer often suffer of long-term xerostomia, and/or consistent shrinkage of parotid glands. In order to avoid these drawbacks, adaptive therapy can be planned for patients at risk, if the pred...

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

Medical image analysis
We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combi...

Epicardial adipose tissue and myocardial ischemia assessed by computed tomography perfusion imaging and invasive fractional flow reserve.

Journal of cardiovascular computed tomography
BACKGROUND: Epicardial adipose tissue (EAT) is a metabolically active fat depot that is associated with incident coronary artery disease (CAD) and major adverse cardiovascular events. The relationship between EAT and myocardial ischemia remains uncle...

Characterizing Architectural Distortion in Mammograms by Linear Saliency.

Journal of medical systems
Architectural distortion (AD) is a common cause of false-negatives in mammograms. This lesion usually consists of a central retraction of the connective tissue and a spiculated pattern radiating from it. This pattern is difficult to detect due the co...

Tomographic image reconstruction via estimation of sparse unidirectional gradients.

Computers in biology and medicine
Since computed tomography (CT) was developed over 35 years ago, new mathematical ideas and computational algorithms have been continuingly elaborated to improve the quality of reconstructed images. In recent years, a considerable effort can be notice...

Multi-atlas and unsupervised learning approach to perirectal space segmentation in CT images.

Australasian physical & engineering sciences in medicine
Perirectal space segmentation in computed tomography images aids in quantifying radiation dose received by healthy tissues and toxicity during the course of radiation therapy treatment of the prostate. Radiation dose normalised by tissue volume facil...

Lung nodule classification using deep feature fusion in chest radiography.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Lung nodules are small, round, or oval-shaped masses of tissue in the lung region. Early diagnosis and treatment of lung nodules can significantly improve the quality of patients' lives. Because of their small size and the interlaced nature of chest ...

Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.

Medical & biological engineering & computing
Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LID...