Medical image based distortion correction mechanism

Abstract

A mobile device to provide a medical image based distortion correction mechanism is described. An image analysis application, executed by the mobile device, captures a digital copy of the medical image with a camera component in response to a user action. Distortion(s) associated with the digital copy are identified by processing the digital copy with deep neural network (DNN) model(s). Next, a manual correction description is determined to correct the distortion(s) in relation to the camera component and the medical image. Furthermore, a notification to recapture the digital copy is provided to the user. The notification includes the manual correction description. Additionally, in response to another user action to correct the distortion(s) or a failure to detect the user execute manual correction(s) associated with the distortion(s) within a time period, the distortion(s) within the digital copy are corrected and the corrected digital copy is provided to the user.

What is claimed is: 
     
         1 . A device to train a deep learning model to correct an image blur in a medical image, wherein the device is configured to:
 receive a training input set of a plurality of training input medical images and a plurality of expected output medical images associated with the plurality of training input medical images;   process the plurality of training input medical images with a deep learning model to correct image blur;   generate a plurality of de-blurred training input medical images; and   train the deep learning model based at least in part on an analysis of the plurality of de-blurred training input medical images and the plurality of expected output medical images.   
     
     
         2 . The device of  claim 1 , wherein each training input medical image includes a medical ultrasound image. 
     
     
         3 . The device of  claim 1 , wherein each training input medical image includes a three dimensional image. 
     
     
         4 . The device of  claim 1 , wherein processing the training input medical images further includes a process to:
 evaluate a metadata of the medical image;   identify an annotation associated with the medical image within the metadata, wherein the annotation designates an averaging process used to generate the medical image from a plurality of scanned images of a scanning session of a biological structure of a patient.   
     
     
         5 . The device of  claim 1 , wherein processing the training input medical images further includes a process to:
 receive a selection of a region of interest (ROI) of the medical image from a user; and   analyze the ROI to identify the image blur within the ROI.   
     
     
         6 . The device of  claim 1 , wherein each training input medical image is processed with the deep learning model in a real-time or offline. 
     
     
         7 . The device of  claim 1 , wherein each training input medical image and a subsequent image of a time sequence based scanning session of a biological structure of a patient are processed with the deep learning model in a real-time or offline. 
     
     
         8 . The device of  claim 1 , wherein the training input set of the deep learning model includes averaged images. 
     
     
         9 . The device of  claim 8 , wherein each of the training input medical images includes a noise reduced average of medical scan images captured during an imaging session. 
     
     
         10 . The device of  claim 9 , wherein one or more edges of an object of interest (OI) within each of the training input medical images is blurred as a result of the noise reduced average of the medical scan images. 
     
     
         11 . The device of  claim 8 , wherein the expected output set of the deep learning model includes de-blurred images corresponding to the averaged images. 
     
     
         12 . The device of  claim 11 , wherein one or more edges of an object of interest ( 01 ) within each of the de-blurred images are sharpened. 
     
     
         13 . The device of  claim 1 , wherein the image provider includes a medical imaging device. 
     
     
         14 . The device of  claim 13 , wherein the medical imaging device is configured to:
 capture the medical image during a capture session to scan a biological structure of a patient.   
     
     
         15 . The device of  claim 1 , wherein the image provider includes a camera component. 
     
     
         16 . The device of  claim 15 , wherein the camera component is configured to:
 capture the medical image from a display device associated with a medical imaging device, wherein the display device is configured to display a scanned image of a biological structure of a patient.   
     
     
         17 . A mobile device for training a deep learning model to correcting an image blur in a medical ultrasound image, the mobile device comprising:
 a memory configured to store instructions associated with an image analysis application,   a processor coupled to the display component, the camera component, and the memory,   the processor executing the instructions associated with the image analysis application, wherein the analysis application includes:
 a neural network module configured to:
 receive a training input set of a plurality of training input medical images and a plurality of expected output medical images associated with the plurality of training input medical images; 
 process the plurality of training input medical images with a deep learning model to correct image blur; 
 generate a plurality of de-blurred training input medical images; and 
 train the deep learning model based at least in part on an analysis of the plurality of de-blurred training input medical images and the plurality of expected output medical images. 
 
   
     
     
         18 . The mobile device of  claim 17 , wherein processing the training input medical images includes one or more operations to:
 identify one or more edges of an object of interest ( 01 ) within the medical ultrasound image, wherein the one or more edges are blurred by the noise reduced average of the ultrasound session images; and   sharpen the one or more edges of the  01  based on the deep learning model.   
     
     
         19 . A method of correcting an image blur in a medical ultrasound image, the method comprising:
 receiving, by a processor, a training input set of a plurality of training input medical images and a plurality of expected output medical images associated with the plurality of training input medical images;   processing, by a processor, the plurality of training input medical images with a deep learning model to correct image blur;   generating, by a processor, a plurality of de-blurred training input medical images; and   training, by a processor, the deep learning model based at least in part on an analysis of the plurality of de-blurred training input medical images and the plurality of expected output medical images.