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 a severity of at least one liver disease from non-invasive radiographic images is disclosed. The method comprises: providing radiographic images of slices of an abdomen of a patient; pre-processing said radiographic images by: segmenting a liver and a spleen, thus achieving a spleen binary mask and a liver binary mask per slice, and normalizing said images with each other, thus achieving normalized radiographic images per slice; for each slice, from the liver binary mask and said 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 the at least one 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 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: 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: To improve a cropping system by obtaining coverage of a wide range of contents for smaller sized displays of handheld devices, a method starts from a metadata aggregation and corresponding video, e.g. in post-production, program exchange and archiving, wherein (a) video is passed to a video analysis to deliver video, e.g. by use of motion detection, morphology filters, edge detection, etc.

Abstract: To improve a cropping system by obtaining coverage of a wide range of contents for smaller sized displays of handheld devices, a method starts from a metadata aggregation and corresponding video, e.g. in post-production, program exchange and archiving, wherein (a) video is passed to a video analysis to deliver video, e.g. by use of motion detection, morphology filters, edge detection, etc.

Abstract: As the amount of audiovisual data that can be received by consumers increases rapidly, there is an increasing need for proper summarisation of audiovisual data like films. Thereto, the invention provides a method of creating a multimedia summary of a stream of audiovisual data like a film. First, a textual summary is retrieved (204). Next, the stream of audiovisual data is segmented (208) and information is extracted from the stream of audiovisual data (210) and the textual summary (206). Finally, segments are selected (212) that carry information matching information carried by the textual summary. Summaries of films and series are abundantly available on the internet and are made by and for devotees, providing a reliable seed for creating a multimedia summary.