US 12,094,190 B2
Systems, methods, and apparatuses for implementing medical image segmentation using interactive refinement
Diksha Goyal, Tempe, AZ (US); and Jianming Liang, Scottsdale, AZ (US)
Assigned to Arizona Board of Regents on behalf of Arizona State University, Scottsdale, AZ (US)
Filed by Arizona Board of Regents on behalf of Arizona State University, Scottsdale, AZ (US)
Filed on Feb. 18, 2022, as Appl. No. 17/675,929.
Claims priority of provisional application 63/151,558, filed on Feb. 19, 2021.
Prior Publication US 2022/0270357 A1, Aug. 25, 2022
Int. Cl. G06V 10/778 (2022.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/774 (2022.01); G06V 10/82 (2022.01)
CPC G06V 10/7788 (2022.01) [G06T 7/0012 (2013.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/7747 (2022.01); G06V 10/82 (2022.01); G06T 2200/24 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/30004 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system comprising:
a memory to store instructions;
a processor to execute the instructions stored in the memory;
wherein the system is specially configured to:
execute instructions via the processor for operating a two-step learning operation via a deep learning training framework having both a base segmentation model and an interCNN model, by performing the following learning operations:
receiving original input images at the deep learning training framework;
generating an initial prediction image specifying image segmentation by processing the original input images through the base segmentation model to render the initial prediction image in the absence of user input guidance signals;
receiving user input guidance signals indicating user-guided segmentation refinements to the initial prediction image;
routing each of (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals to an InterCNN;
generating a refined prediction image specifying refined image segmentation by processing each of the (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals through the InterCNN to render the refined prediction image incorporating the user input guidance signals; and
outputting a refined segmentation mask based on application of the user input guidance signals to the deep learning training framework as a guidance signal.