Siemens Healthineers International Ag

Alexander Maslowski

Todd Arlin Wareing

Douglas Allen Barnett

Esa Kuusela

Benjamin M Haas

Hannu Laaksonen

Maria Cordero Marcos

Shahab Basiri

Elena Czeizler

Angelo Genghi

Simon Mathias Fluckiger

Anri Friman

A control circuit accesses a radiation treatment plan for a given patient. The control circuit then generates dose volume histogram information as a function of the radiation treatment plan and automatically assesses the dose volume histogram information to identify any anomalous results. Generating that information can comprise, at least in part and for example, generating at least one dose volume histogram curve. The latter may comprise generating at least one dose volume histogram curve for each of a plurality of different patient structures (such as one or more treatment volumes and/or one or more organs-at-risk).

Detecting anomalous dose volume histogram information

Embodiments described herein provide for training an artificial intelligence model to boost dose depositions. The artificial intelligence model receives medical images and a dose deposition determined according to a first dose deposition model. The artificial intelligence model modifies the received dose deposition determined according to the first dose deposition model such that the dose deposition simulates a dose deposition determined by a second dose deposition model.

Artificial intelligence boosting dose calculations

Embodiments described herein provide for coupling Monte Carlo source modeling with deterministic dose calculations. An internal volumetric first scatter distributed source of a patient is determined using Monte Carlo simulations and ingested into one or more dosing algorithms. The dosing algorithms use the source model to determine a dose deposition.

Driving deterministic dose depositions with monte carlo source modeling

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

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Peachtree City, GA, United States of America

Alexander Maslowski