# US 2025/0375167 A1 · Method and system for determining post-operative images of an anomaly using deep-learning models > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2025/0375167 A1 - Document ID: US20250375167A1 - Status: Published application - Kind: A1 - Application number: 18/944,084 - Filed: 2024-11-12 - Published: 2025-12-11 - Earliest claimed: 2024-06-06 - Assignees: L&T Technology Services Limited - Applicants: L&T Technology Services Limited - Inventors: RAKSHITH MANJUNATH; NISARGA KRISHNEGOWDA - Web page: https://patentgrep.com/patent/US20250375167A1 ## CPC - A61B 5/7267: {involving training the classification device} - A61B 5/055: involving electronic [EMR] or nuclear [NMR] magnetic resonance, e.g. magnetic resonance imaging - G16H 30/40: for processing medical images, e.g. editing - A61B 2505/05: Surgical care - A61B 2576/00: Medical imaging apparatus involving image processing or analysis (A61B1/00009, A61B6/52 and A61B8/52 take precedence) - G06N 3/045: Combinations of networks - G06N 3/0455: Auto-encoder networks; Encoder-decoder networks - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/047: Probabilistic or stochastic networks - G06N 3/0475: Generative networks - G06N 3/094: Adversarial learning - G06T 7/11: Region-based segmentation - G06T 2207/10088: Magnetic resonance imaging [MRI] - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL ## Abstract A method and a system for determining final post-operative images of an anomaly is disclosed. A processor inputs a pre-operative image of the anomaly to a first GAN, a second GAN, and a third GAN. Each of the first, second and third GANs are trained based on a training data that includes a training set of post-operative images of the anomaly corresponding to a training set pre-operative images of the anomaly. Further, a first post-operative image of the anomaly is determined from the first GAN, a second post-operative image of the anomaly is determined from the second GAN and a third post-operative image of the anomaly is determined from the third GAN. Two of the first, the second and the third post-operative images are selected based on a SSIM score of each of the first, the second and the third post-operative images. ## Claims ### Claim 1 A method of determining post-operative image of an anomaly, comprising: simultaneously inputting, by a processor, 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, by the processor, a first post-operative image of the anomaly from the first generative adversarial network (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, by the processor, 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, by the processor, 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. ### Claim 2 (depends on 1) The method of claim 1, wherein the SSIM score is determined based on texture, luminance and contrast of each of the first post-operative image, the second post-operative image and the third post-operative image. ### Claim 3 (depends on 1) The method of claim 1, wherein the training set of pre-operative images of the anomaly are determined from a set of pre-operative MRI images of the body part, wherein the training set of post-operative images of the anomaly are determined from a set of post-operative MRI images of the body part, and wherein each of the set of pre-operative MRI images and each of the set of post-operative MRI images capture the body part from a predefined angular view with respect to a central axis of the body part. ### Claim 4 (depends on 3) The method of claim 3, wherein the training set of pre-operative images and the training set of post-operative images are determined using an encoder-decoder convolutional neural network pretrained to detect the abnormality in the body part captured in the set of pre-operative MRI images and the set of post-operative MRI images respectively. ### Claim 5 (depends on 3) The method of claim 3, wherein each of the set of pre-operative MRI images and the set of post-operative MRI images are resized to a predefined size. ### Claim 6 (depends on 3) The method of claim 3, wherein pixel intensity values of each of the set of pre-operative MRI images and the set of post-operative MRI images are normalized between 0 and 1. ### Claim 7 (depends on 1) The method of claim 1, wherein the third GAN measures a similarity score index of the third post-operative image with the training set of post-operative images of the anomaly by using a critic model. ### Claim 8 (depends on 1) The method of claim 1, wherein the determination of the final post-operative image comprises: detecting, by the processor, edges of the selected two of the first post-operative image, the second post-operative image and the third post-operative image; reducing, by the processor and upon detection of the edges, noise of the selected two of the first post-operative image, the second post-operative image and the third post-operative image using a gaussian filter to determine corresponding two smoothened images; and assigning, by the processor, weights to pixels of the corresponding two smoothened images, wherein the final post-operative image is determined based on pixel-wise aggregation of the corresponding two smoothened images based on the weights assigned to pixels with higher intensity. ### Claim 9 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. ### Claim 10 (depends on 9) The system of claim 9, wherein the SSIM score is determined based on texture, luminance and contrast of each of the first post-operative image, the second post-operative image and the third post-operative image. ### Claim 11 (depends on 9) The system of claim 9, wherein the training set of pre-operative images of the anomaly are determined from a set of pre-operative MRI images of the body part, wherein the training set of post-operative images of the anomaly are determined from a set of post-operative MRI images of the body part, and wherein each of the set of pre-operative MRI images and each of the set of post-operative MRI images capture the body part from a predefined angular view with respect to a central axis of the body part. ### Claim 12 (depends on 11) The system of claim 11, wherein the training set of pre-operative images and the training set of post-operative images are determined using an encoder-decoder convolutional neural network pretrained to detect the abnormality in the body part captured in the set of pre-operative MRI images and the set of post-operative MRI images respectively. ### Claim 13 (depends on 11) The system of claim 11, wherein each of the set of pre-operative MRI images and the set of post-operative MRI images are resized to a predefined size. ### Claim 14 (depends on 11) The system of claim 11, wherein pixel intensity values of each of the set of pre-operative MRI images and the set of post-operative MRI images are normalized between 0 and 1. ### Claim 15 (depends on 9) The system of claim 9, wherein the third GAN measures a similarity score index of the third post-operative image with the training set of post-operative images of the anomaly by using a critic model. ### Claim 16 (depends on 9) The system of claim 9, wherein to determine the final post-operative image, the processor is configured to: detect edges of the selected two of the first post-operative image, the second post-operative image and the third post-operative image; upon the detection of the edges, reduce noise of the selected two of the first post-operative image, the second post-operative image and the third post-operative image using a gaussian filter to determine corresponding two smoothened images; assign weights to pixels of the corresponding two smoothened images, wherein the final post-operative image is determined based on pixel-wise aggregation of the corresponding two smoothened images based on the weights assigned to pixels with higher intensity. ### Claim 17 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. ### Claim 18 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the SSIM score is determined based on texture, luminance and contrast of each of the first post-operative image, the second post-operative image and the third post-operative image. ### Claim 19 (depends on 17) The non-transitory computer-readable medium of claim 17, wherein the training set of pre-operative images of the anomaly are determined from a set of pre-operative MRI images of the body part, wherein the training set of post-operative images of the anomaly are determined from a set of post-operative MRI images of the body part, and wherein each of the set of pre-operative MRI images and each of the set of post-operative MRI images capture the body part from a predefined angular view with respect to a central axis of the body part. ### Claim 20 (depends on 19) The non-transitory computer-readable medium of claim 19, wherein the training set of pre-operative images and the training set of post-operative images are determined using an encoder-decoder convolutional neural network pretrained to detect the abnormality in the body part captured in the set of pre-operative MRI images and the set of post-operative MRI images respectively.