Abstract: Example methods for adaptive radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining treatment image data associated with a first imaging modality. The treatment image data may be acquired during a treatment phase of a patient. Also, planning image data associated with a second imaging modality may be acquired prior to the treatment phase to generate a treatment plan for the patient. The method may also comprise: in response to determination that an update of the treatment plan is required, transforming the treatment image data associated with the first imaging modality to generate transformed image data associated with the second imaging modality. The method may further comprise: processing, using the deep learning engine, the transformed image data to generate output data for updating the treatment plan.

Abstract: Example methods for radiotherapy treatment planning using deep learning engines are provided. One example method may comprise obtaining first image data associated with a patient; generating first feature data by processing the first image data associated with a first resolution level using a first processing pathway; generating second feature data by processing second image data associated with a second resolution level using a second processing pathway; and generating third feature data by processing third image data associated with a third resolution level using a third processing pathway. The example method may also comprise generating a first combined set of feature data associated with the second resolution level, and a second combined set of feature data associated with the first resolution level based on the first feature data and the first combined set. Further, the example method may comprise generating output data associated with radiotherapy treatment of the patient.