公开(公告)号：US12094190B2

公开(公告)日：2024-09-17

申请号：US17675929

申请日：2022-02-18

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Diksha Goyal ,  Jianming Liang

IPC: G06V10/778 ,  G06T7/00 ,  G06T7/11 ,  G06T7/194 ,  G06V10/774 ,  G06V10/82

CPC classification number: G06V10/7788 ,  G06T7/0012 ,  G06T7/11 ,  G06T7/194 ,  G06V10/7747 ,  G06V10/82 ,  G06T2200/24 ,  G06T2207/20081 ,  G06T2207/20084 ,  G06T2207/20092 ,  G06T2207/30004

Abstract: Medical image segmentation using interactive refinement, in which the trained deep models are then utilized for the processing of medical imaging are described. Operating a two-step deep learning training framework including receiving original input images at the deep learning training framework; generating an initial prediction image specifying image segmentation by base segmentation model; receiving user input guidance signals; 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 to the deep learning training framework as a guidance signal.

公开(公告)号：US20220270357A1

公开(公告)日：2022-08-25

申请号：US17675929

申请日：2022-02-18

Applicant: Arizona Board of Regents on behalf of Arizona State University

Inventor： Diksha Goyal ,  Jianming Liang

IPC: G06V10/778 ,  G06T7/194 ,  G06V10/82 ,  G06V10/774 ,  G06T7/00 ,  G06T7/11

Abstract: Described herein are means for implementing medical image segmentation using interactive refinement, in which the trained deep models are then utilized for the processing of medical imaging. For instance, an exemplary system is specially configured for operating a two-step deep learning training framework including means for receiving original input images at the deep learning training framework; means for 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; means for receiving user input guidance signals indicating user-guided segmentation refinements to the initial prediction image; means for routing each of (i) the original input images, (ii) the initial prediction image, and (iii) the user input guidance signals to an InterCNN; means for 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 means for outputting a refined segmentation mask based on application of the user input guidance signals to the deep learning training framework as a guidance signal. Other related embodiments are disclosed.