Method and system for determining post-operative images of an anomaly using deep-learning models

A system for determining post-operative images of an anomaly, comprising:

• a processor;
• and
• a memory communicably coupled to the processor, wherein the memory stores processor-executable instruction, which, on executing by the processor cause the processor to:
• simultaneously input pre-operative image of an anomaly detected in an internal body part to a first generative adversarial network (GAN), a second GAN and a third GAN;
• determine a first post-operative image of the anomaly from the first GAN, a second post-operative image of the anomaly from the second GAN and a third post-operative image of the anomaly from the third GAN,
• wherein each of the first GAN, the second GAN and the third GAN are trained based on a training data comprising a training set of post-operative images of the anomaly corresponding to a training set pre-operative images of the anomaly;
• select two of the first post-operative image, the second post-operative image and the third post-operative image based on a Structural Similarity Index Measure (SSIM) score of each of the first post-operative image, the second post-operative image and the third post-operative image;
• and
• determine a final post-operative image of the anomaly by performing a pixel-wise aggregation of the selected two of the first post-operative image, the second post-operative image and the third post-operative image.

A non-transitory computer-readable medium storing computer-executable instructions for determining post-operative image of an anomaly, the computer-executable instructions configured for:

• simultaneously inputting a pre-operative image of the anomaly, detected in an internal body part of a patient, to a first generative adversarial network (GAN), a second GAN and a third GAN;
• determining a first post-operative image of the anomaly from the first generative adversarial (GAN), a second post-operative image of the anomaly from the second GAN and a third post-operative image of the anomaly from the third GAN,
• wherein each of the first GAN, the second GAN and the third GAN are trained based on a training data comprising a training set of post-operative images of the anomaly corresponding to a training set of pre-operative images of the anomaly;
• selecting two of the first post-operative image, the second post-operative image and the third post-operative image based on a Structural Similarity Index Measure (SSIM) score of each of the first post-operative image, the second post-operative image and the third post-operative image;
• and
• determining a final post-operative image of the anomaly by performing a pixel-wise aggregation of the selected two of the first post-operative image, the second post-operative image and the third post-operative image.