Correcting image blur in medical image

Abstract

A device to correct an image blur within a medical image is described. An image analysis application executed by the device receives the medical image from a medical image provider. Next, the image blur is detected within the medical image by analyzing the medical image. The medical image is subsequently processed with a deep learning model to correct the image blur. In response to the processing, a de-blurred medical image is generated. The de-blurred medical image is provided for a presentation or a continued analysis.

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 (OI) 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 ((ill) 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 OI 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.