Neural network-based radiation treatment planning
AssigneeSiemens Healthineers International AG
InventorsRiqiang Gao, Bin Lou, Ali Kamen
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Abstract
A neural network is trained using a training corpus having a plurality of information features, each of the information features including both a reference radiation treatment dose and at least one corresponding post-treatment patient datum. By one approach, the at least one corresponding post-treatment patient datum comprises patient imagery such as, but not limited to, one or more computed tomography images. That trained neural network facilitates radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information.
Claims17
A method to train a neural network configured to facilitate radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information, the method comprising:
- accessing a training corpus comprising a plurality of information features, each of the information features comprising both a reference radiation treatment dose and at least one corresponding post-treatment patient datum;
- training the neural network using the training corpus;
- wherein the neural network comprises a probabilistic model that uses probability theory to make predictions and to classify inputs.
An apparatus comprising:
- a control circuit configured as a neural network that facilitates radiation treatment planning by generating resultant treatment efficacy probability information and resultant treatment complications probability information, wherein the neural network was trained with a training corpus comprising a plurality of information features, each of the information features comprising both a reference radiation treatment dose and at least one corresponding post-treatment patient datum, and wherein the neural network comprises a probabilistic model that uses probability theory to make predictions and to classify inputs.
Dependencies are inferred from the claim text.