Automatic computation of BPE level

An image processing system, comprising:

• a display device;
• an x-ray imaging system configured to perform dual-energy imaging;
• non-transitory memory storing instructions executable by one or more processors to:
• train a deep learning (DL) model with a plurality of sets of training data from training subjects, each set of training data of the plurality of sets of training data including two input images of a breast of a respective one of the training subjects, ground truth versions of the two input images with background parenchymal enhancement (BPE) segmented, and a ground truth BPE score for both of the two input images, the two input images comprising a training low energy image and a training recombination image created from the low energy image and a training high energy image;
• receive a first set of medical images of a first breast of a patient acquired via the x-ray imaging system, the first set of medical images of the first breast including a low energy image and a first high energy image;
• input the the first low energy image and a first recombination image generated from the low energy image and the first high energy image into the DL model, the DL model trained to perform an assessment of a first level of BPE of the first breast based on the first low energy image and the first recombination image;
• receive a first BPE assessment of the first breast as an output of the DL model, the first BPE assessment including at least one of a score and a classification indicating a percentage of glandular tissues of the first breast showing BPE with respect to a surface or volume of the first breast;
• and
• display the first BPE assessment on the display device.

A method for an x-ray imaging system configured to perform dual-energy imaging, the method comprising:

• training a deep learning (DL) model with a plurality of sets of training data from training subjects, each set of training data of the plurality of sets of training data including two input images of a breast of a respective one of the training subjects, ground truth versions of the two input images with background parenchymal enhancement (BPE) segmented, and a ground truth BPE score for both of the two input images, the two input images comprising a training low energy image and a training recombination image created from the training low energy image and a training high energy image;
• receiving a plurality of medical images of breasts of a patient acquired via the x-ray imaging system during a breast exam of the patient, the x-ray imaging system configured to obtain different views of the breasts at different times of the breast exam, the plurality of medical images including a first set of medical images of a first breast of the patient acquired via the imaging system at a first time of the breast exam and a second set of medical images of a second breast of the patient acquired via the imaging system at a second time of the breast exam, wherein the first set of medical images includes a first low energy image and a first high energy image, and wherein the second set of medical images includes a second low energy image and a second high energy image;
• inputting the first low energy image, the second low energy image, a first recombination image generated from the first low energy image and the first high energy image, a second recombination image generated from the second low energy image and the second high energy image, the first time, and the second time into the DL model to obtain an assessment of a BPE asymmetry between the first breast and the second breast;
• displaying at least one image of the first set of medical images of the first breast on a display device of the imaging system;
• displaying at least one image of the second set of medical images of the second breast on the display device;
• and
• displaying an indication of the BPE asymmetry on the display device.