Quibim, S.l.

Universidad De Zaragoza

Fundación Para La Investigación Del Hospital Universitario La Fe De La Comunidad Valenciana

Fabio García Castro

Angel Alberich Bayarri

Luis Martí Bonmatí

Rafael López González

Belén Fos Guarinos

Ana Maria Jiménez Pastor

Amadeo Ten Esteve

María Ángeles Pérez Ansón

The present invention relates to a method and a system for identifying an anomaly in a chest X-ray image in posteroanterior orientation based on neural networks, which comprises: receiving the image in a classification module with a plurality of convolutional neural networks, each trained to identify at least one specific graphic pattern associated with a pathology; obtaining, for each of the convolutional neural networks, a probability of detecting the specific graphic pattern; receiving in a fully connected neural network all the probabilities obtained; and determining whether the chest X-ray image contains an anomaly, based on the detection probabilities provided.

Method and system for identifying anomalies in X-rays

A method is disclosed for obtaining an image biomarker that quantifies the quality of the trabecular structure of bones. The method includes: retrieving high-resolution CT (Computed Tomography) and/or MRI (Magnetic Resonance Imaging) trabecular images from an image database; pre-processing and post-processing the high-resolution CT and/or MRI trabecular images and obtaining the unique image biomarker 'QTS'. Pre-processing may include: calculating the region of interest “ROI”; calculating the bone fraction map; eliminating the partial volume effect; and, binarizing. Post-processing may include: skeletonisation and extraction of morphological and structural characteristics. Lastly, the unique image biomarker “QTS” is defined as:QTS=0.7137*Comp1+0.2863*Comp2.

Method for obtaining an image biomarker that quantifies the quality of the trabecular structure of bones

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Valencia, Spain

Fabio García Castro