Varian Medical Systems International Ag

Siemens Healthineers International Ag

Varian Medical Systems, Inc.

Thomas Coradi

Benjamin M Haas

Tomasz Morgas

Tobias Gass

Marco Lessard

Jonas Honegger

Christopher Boylan

Patrik Kunz

Michael Waschbuesch

Martin Scholz

Fernando Franco

Janne Iimari Nord

Angelo Genghi

Mário Joao Fartaria

Simon Mathias Fluckiger

Anna Siroki-Galambos

Micheal Waschbuesch

Michael Waschbüesch

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

A computer-implemented method of generating dose information for a region of patient anatomy includes determining a first set of dose values for a first three-dimensional (3D) image of the region, wherein each value in the first set of dose values is associated with a different voxel of the first 3D image, and wherein the first 3D image is associated with a specific application of dose to the region; determining a second set of dose values for a representative 3D image of the region, wherein each value in the second set of dose values is associated with a different voxel of the representative 3D image; determining a set of geometric error models for the representative 3D image of the region, wherein each geometric error model in the set of geometric error models indicates a geometric error between a voxel of the representative 3D image and one or more voxels of a treatment fraction 3D image of the region; and based on the second set of dose values and the set of geometric error models, determining a set of dose probability values for each voxel of the representative 3D image, wherein each set of dose probability values includes at least one dose value and a probability value that corresponds to the dose value.

Determining uncertainty in radiation therapy delivered dose accumulation

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

To overcome the difficulties inherent in conventional treatment planning approaches, new techniques are described herein for providing an intuitive user interface for automatic structure derivation in patient modeling. In an embodiment, a graphical user interface is provided that provides a list of structures of a specified region. The interface uses medical terminology instead of mathematical one. In one or more embodiments, the list of structures may be a pre-defined list of structures that correspond to that region for the purposes of treatment planning. A user is able to actuate a toggle to include and/or exclude each of the structures separately. In one or more embodiments, the user is also able to actuate a toggle to define a perimeter around each included structure, and further define a margin around the perimeter. The user is also able to specify whether the desired output should include a union or the intersection of all included structures.

Intuitive automation in patient modeling

An apparatus and method for determining an image similarity based on image features. In one aspect, the image similarity determination is based on an image comparison tool. The image comparison tool may be trained, by a machine-learning system, to estimate a similarity between images based on a subset of image data comprised by image features. The estimate may be an estimate of how similar structures found in the images would be following a geometric transformation of some of the structures. In one aspect, an atlas image for performing automatic segmentation of an image is determined according to a comparison made using the image comparison tool.

Image comparison tool tolerant to deformable image matching

A shape similarity metric can be provided that indicates how similar two or more shapes are. A difference between a union of the shapes and an intersection of the shapes can be used to determine the similarity metric. The shape similarity metric can provide an average distance between the shapes. Different processes for determining shapes can be evaluated for accuracy based on the shape similarity metric. New or alternative shape-determining processes can be compared for accuracy against other shape-determining processes including reference shape-determining processes. Shape similarity metrics can be determined for two-dimensional shapes and three-dimensional shapes.

Shape similarity measure for body tissue

Growing community of inventors

Lenzburg, Switzerland

Thomas Coradi