Systems and methods for coordinating execution of an ensemble of machine learning models

A method for coordinating execution of an ensemble of machine learning models to determine anatomical structures to target during cancer treatment, the method comprising:

• executing, by one or more processors, a lesion detection model based on functional image data associated with a PET image of a patient, the lesion detection model configured to generate a first output comprising first lesion labels that indicate points within the patient associated with metabolic activity indicative of tumor lesions;
• executing, by the one or more processors, a masking model based on structural image data associated with a CT image of the patient, the masking model configured to generate a second output comprising a plurality of anatomical masks that identify volumes within the patient that correspond to anatomical structures;
• correlating, by the one or more processors, a first set of points within the CT image that indicate at least one first lesion based on a correspondence between the PET image and the CT image;
• correlating, by the one or more processors, a second set of points within the CT image that indicate at least one second lesion based on a cropped portion of the PET image and the correspondence between the PET image and the CT image;
• and
• determining, by the one or more processors, a set of anatomical structures that correspond to the at least one first lesion and the at least one second lesion based on locations associated with the first set of points and the second set of points relative to the plurality of anatomical masks.

A non-transitory computer-readable medium storing instructions thereon that, when executed by one or more processors, cause the one or more processors to:

• execute a lesion detection model based on functional image data associated with a PET image of a patient, the lesion detection model configured to generate a first output comprising first lesion labels that indicate points within the patient associated with metabolic activity indicative of tumor lesions;
• execute a masking model based on structural image data associated with a CT image of the patient, the masking model configured to generate a second output comprising a plurality of anatomical masks that identify volumes within the patient that correspond to anatomical structures;
• correlate a first set of points within the CT image that indicate at least one first lesion based on a correspondence between the PET image and the CT image;
• correlate a second set of points within the CT image that indicate at least one second lesion based on a cropped portion of the PET image and the correspondence between the PET image and the CT image;
• and
• determine a set of anatomical structures that correspond to the at least one first lesion and the at least one second lesion based on locations associated with the first set of points and the second set of points relative to the plurality of anatomical masks.