公开(公告)号：US12272063B2

公开(公告)日：2025-04-08

申请号：US18807820

申请日：2024-08-16

Applicant: Median Technologies

Inventor： Benoit Huet ,  Pierre Baudot ,  Elias Munoz ,  Ezequiel Geremia ,  Jean-Christophe Brisset ,  Vladimir Groza

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/62 ,  G06V10/26 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774

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. The method for digital image processing further comprises: receiving another image; segmenting, generating a mesh, computing quantifiable visual parameters, and extracting quantifiable visual data; and classifying the extracted quantifiable visual data with the trained computing operation.

公开(公告)号：US11810299B1

公开(公告)日：2023-11-07

申请号：US18351699

申请日：2023-07-13

Applicant: MEDIAN TECHNOLOGIES

Inventor： Benoît Huet ,  Danny Francis ,  Pierre Baudot

IPC: G06T7/00 ,  G06V10/25 ,  G06V10/26 ,  G06V10/774 ,  G06V10/82 ,  G06T7/73 ,  G06V10/32 ,  G06V10/776 ,  G16H50/20

CPC classification number: G06T7/0012 ,  G06T7/73 ,  G06V10/25 ,  G06V10/26 ,  G06V10/32 ,  G06V10/774 ,  G06V10/776 ,  G06V10/82 ,  G16H50/20 ,  G06T2200/04 ,  G06T2207/10081 ,  G06T2207/10088 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/30096 ,  G06V2201/03

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.

公开(公告)号：US20230316507A1

公开(公告)日：2023-10-05

申请号：US18040930

申请日：2021-08-26

Applicant: MEDIAN TECHNOLOGIES

Inventor： Nozha BOUJEMAA ,  Benoit HUET ,  Vladimir GROZA ,  Danny FRANCIS

IPC: G06T7/00 ,  G16H30/40 ,  G16H50/30

CPC classification number: G06T7/0012 ,  G16H30/40 ,  G16H50/30 ,  G06T2207/20084 ,  G06T2207/30096

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.

公开(公告)号：US20240412363A1

公开(公告)日：2024-12-12

申请号：US18807820

申请日：2024-08-16

Applicant: Median Technologies

Inventor： Benoit Huet ,  Pierre Baudot ,  Elias Munoz ,  Ezequiel Geremia ,  Jean-Christophe Brisset ,  VIadimir Groza

IPC: G06T7/00 ,  G06T7/11 ,  G06T7/62 ,  G06V10/26 ,  G06V10/764 ,  G06V10/77 ,  G06V10/774

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. The method for digital image processing further comprises: receiving another image; segmenting, generating a mesh, computing quantifiable visual parameters, and extracting quantifiable visual data; and classifying the extracted quantifiable visual data with the trained computing operation.

公开(公告)号：US20230222771A1

公开(公告)日：2023-07-13

申请号：US18151906

申请日：2023-01-09

Applicant: MEDIAN TECHNOLOGIES

Inventor： Hubert BEAUMONT ,  Tiffany FORIEL ,  Vincent BOBIN ,  Antoine IANNESSI

IPC: G06V10/764 ,  G06V10/25 ,  G06V10/26 ,  G06V10/762 ,  G06V10/44

CPC classification number: G06V10/764 ,  G06V10/25 ,  G06V10/26 ,  G06V10/762 ,  G06V10/44 ,  G06V2201/031 ,  A61B5/055

Abstract: A method and system are disclosed for generating a machine learning model for automatic classification of radiographic images acquired by various acquisition protocols. The method includes the steps of: providing a plurality of radiographic images, detecting and segmenting in each of the radiographic image at least one regions of interest (ROI) as reference ROI, measuring at least one radiomic feature per reference ROI, identifying valid reference ROIs based on the measured radiomics values, and clustering the measured radiomics values of valid reference ROIs into at least two reference clusters according to a set of characteristics of image acquisition. A method and system are disclosed for classifying radiographic images by applying a machine learning model generated for automatic classification of radiographic images.

公开(公告)号：US20220414870A1

公开(公告)日：2022-12-29

申请号：US17772881

申请日：2020-11-05

Applicant: MEDIAN TECHNOLOGIES

Inventor： Elton REXHEPAJ ,  Corinne RAMOS ,  Nozha BOUJEMAA ,  Jean-Christophe BRISSET ,  Pierre BAUDOT ,  Sébastien POULLOT ,  Benjamin RENOUST ,  Benoit HUET

IPC: G06T7/00 ,  G06T7/11

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.