Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Benjamin M Haas

Thomas Coradi

Tomasz Morgas

Tobias Gass

Marco Lessard

Jonas Honegger

Pascal Paysan

Christopher Boylan

Janne Iimari Nord

Angelo Genghi

Joakim Pyyry

Sami Petri Perttu

Dieter Marc Seghers

Armel C Rosselet

Patrik Kunz

Michael Waschbuesch

Michael Waschbusch

Martin Scholz

Simon Mathias Fluckiger

David Kleiner

Alexander Maslowski

Fernando Franco

Mário Joao Fartaria

Yarema Mazuryk

Micheal Waschbuesch

Pawel Zak

Joao Neta

Anna Siroki-Galambos

Anri Friman

Systems and method for automatically generating structures, such as target volumes, in a treatment image using structure-guided deformation to propagate the structures from a planning image onto the subsequently acquired treatment image.

Systems, methods and devices for automated target volume generation

Systems and methods for augmenting a training data set with annotated pseudo images for training machine learning models. The pseudo images are generated from corresponding images of the training data set and provide a realistic model of the interaction of image generating signals with the patient, while also providing a realistic patient model. The pseudo images are of a target imaging modality, which is different than the imaging modality of the training data set, and are generated using algorithms that account for artifacts of the target imaging modality. The pseudo images may include therein the contours and/or features of the anatomical structures contained in corresponding medical images of the training data set. The trained models can be used to generate contours in medical images of a patient of the target imaging modality or to predict an anatomical condition that may be indicative of a disease.

Systems and methods for pseudo image data augmentation for training machine learning models

Embodiments described herein provide for training a machine learning model for automatic organ segmentation. A processor executes a machine learning model using an image to output at least one predicted organ label for a plurality of pixels of the image. Upon transmitting the at least one predicted organ label to a correction computing device, the processor receives one or more image fragments identifying corrections to the at least one predicted organ label. Upon transmitting the one or more image fragments and the image to a plurality of reviewer computing devices, the processor receives a plurality of inputs indicating whether the one or more image fragments are correct. When a number of inputs indicating an image fragment of the image fragments is correct exceeds a threshold, the processor aggregates the image fragment into a training data set. The processor trains the machine learning model with the training data set.

Systems and methods for refining training data

Embodiments described herein provide a method for generating training data for Al based atlas mapping slice localization, and a system and method for using the training data to train a deep learning network. Training data development maps each slice of an input medical image to a position in a full body reference atlas along the longitudinal body axis. The method constructs a landmarking table of 2D slices indicating known anatomic landmarks of a reference subject, and interpolated slices. A final step for obtaining training data uses regression analysis techniques to create a vector of longitudinal axis coordinates of all slices from the input image. The training data is used to train a deep learning model to create an AI-based atlas mapping slice localizer model. The trained AI-based atlas mapping slice localizer model can be applied to generate mapping inputs to autosegmentation models to improve efficiency and reliability of contouring.

AI-based atlas mapping slice localizer for deep learning autosegmentation

Systems and methods for implementing an adaptive therapy workflow that minimizes time needed to create a session patient model, select an appropriate plan for the treatment session, and treat the patient.

Streamlined, guided on-couch adaptive workflow

Example methods and systems for tomographic data analysis are provided. One example method may comprise: obtaining first three-dimensional (3D) feature volume data and processing the first 3D feature volume data using an AI engine that includes multiple first processing layers, an interposing forward-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating second 3D feature volume data by processing the first 3D feature volume data using the multiple first processing layers, transforming the second 3D volume data into 2D feature data using the forward-projection module and generating analysis output data by processing the 2D feature data using the multiple second processing layers.

Tomographic image analysis using artificial intelligence (AI) engines

Example methods and systems for tomographic image reconstruction are provided. One example method may comprise: obtaining two-dimensional (2D) projection data and processing the 2D projection data using the AI engine that includes multiple first processing layers, an interposing back-projection module and multiple second processing layers. Example processing using the AI engine may involve: generating 2D feature data by processing the 2D projection data using the multiple first processing layers, reconstructing first three-dimensional (3D) feature volume data from the 2D feature data using the back-projection module; and generating second 3D feature volume data by processing the first 3D feature volume data using the multiple second processing layers.

Tomographic image reconstruction using artificial intelligence (AI) engines

Techniques for the mapping, selection and import of patient medical objects can include receiving metadata of a plurality of patient medical objects from one or more data sources. Metadata icons can be generated for the received metadata of the plurality of patient medical objects. The metadata icons can be filtered based on one or more received criteria. A patient map including the filtered metadata icons can be generated. In addition, the selection of one or more metadata icons in the patient map can be received and used to determine one or more selected patient medical objects. The selected patient medical objects can thereafter be imported from the one or more data sources.

Patient object mapping techniques

Growing community of inventors

Roggwil, Switzerland

Benjamin M Haas