Abstract: An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data.

Abstract: A method of patient stratification between respondents and non-respondents to immuno-oncology (IO). This method, based on deep-learned features extracted owing to automatic AI-based models that have been fully-trained, goes beyond traditional radiomic standards, opening new perspective for a broader uptake of machine learning solutions in both patient care and drug development. Based on latest Machine Learning advances, the here proposed method allows predicting non-invasively a patient's tumor response to immuno-oncology therapy based treatment. The here proposed method operates not only on early stage conditions though a whole organ and lesion-agnostic analysis for prediction, but also on advanced metastatic stages through a multi-organ analysis performing a disease-agnostic and stage-agnostic prediction, potentially in accordance with response criteria defined by the RECIST 1.1 evaluation methodology.

Abstract: A method for performing classification of the severity of at least one liver disease from non-invasive radiographic images is disclosed. The method includes: providing radiographic images of slices of the abdomen of a patient; pre-processing the radiographic images by: segmenting liver and spleen, thus achieving a spleen binary mask and a liver binary mask per slice, and normalizing the images with each other, thus achieving normalized radiographic images per slice; for each slice, from the liver binary mask and the normalized radiographic images, extracting a liver parameter; from at least one spleen binary mask, extracting a spleen parameter; and classifying, in function of both parameters and by help of a trained Machine Learning model, the severity of liver disease between one among a group of liver disease at early stage and a group of liver disease at advanced stage.

Abstract: An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data.

Abstract: A method for generating a machine learning model for characterizing a plurality of Regions Of Interest ROIs based on a plurality of 3D medical images and an associated method for characterizing a Region Of Interest ROI based on at least one 3D medical image. The methods proposed here aim to provide complementary strategies to enable a classification of ROIs from 3D medical images which could take profit of the advantageous and complementarity of both 2D and 3D CNNs to improve the accuracy of the prediction. More precisely, the present disclosure proposes a 2D model that complements the 3D model so that the sensitivity/specificity of the diagnosis is improved by taking advantage of complementary notions.

Abstract: An image processing apparatus for computing a quantitative imaging biomarker (QIB) of disease severity from variations in texture-based features in a tomographic image, the apparatus including a first preprocessing module for normalizing the intensities in the tomographic image; a second identification module for identifying at least one organ of interest in the tomographic image; a third ROI selection module for identifying and selecting a plurality of target ROIs and reference ROIs representative respectively of abnormal and normal pathology in the organ(s) of interest; a fourth ROI assessment module for extracting a plurality of texture-based feature signatures from the target ROIs and the reference ROIs, wherein the feature signatures are generated from distributions of statistical attributes extracted from each ROI; a fifth biomarker assessment module for computing the distance between the target ROI signatures and the reference ROI signatures, wherein the biomarker of disease severity is a function of t

Abstract: A system and method for the automated classification of lesions in CT images of the chest between measurable and non-measurable lesions is disclosed. The method comprises the steps of identifying lesions in a CT image, performing repeated measurements of selected metrics on the identified lesions and selecting as measurable lesions those with a variability of less than a pre-defined limit of agreement. Then a training step is carried out relying on a variety of image related features extracted from the lesions. Finally, labeling of lesions according to their likelihood of being consistently measured is performed.