The invention relates to the technical field of
deep learning, in particular to a radiotherapy plan
dose distribution
verification method based on
deep learning, and the method comprises the following steps: collecting historical radiotherapy plan data, generating a physical
reference dose field through a Monte Carlo
algorithm, unifying the
voxel resolution of an anatomical structure to 1
cubic millimeter, and normalizing the
dose according to a prescription, data enhancement is carried out only by adopting translation and mirror transformation, trace
Gaussian noise is added, and a
physical information enhanced three-dimensional training
data set is constructed. According to the method, a three-dimensional convolutional network is utilized to automatically learn a
dose distribution rule of a historical high-quality plan, a physical constraint module is embedded to ensure that a prediction result accords with a
radiology principle, a real-time clinical
rule engine is combined to instantly identify and correct a violation hot spot cold region, and an
uncertainty quantification technology is assisted to position a high-risk region, so that the accuracy of a prediction result is improved. Finally, minute-level full-automatic
verification is achieved,
executable optimization suggestions are output, and efficiency is improved by dozens of times while safety is improved.