US 12,141,966 B2
AI-based atlas mapping slice localizer for deep learning autosegmentation
Angelo Genghi, Wettingen (CH); Anna Siroki-Galambos, Allschwil (CH); Thomas Coradi, Lenzburg (CH); Mário Joao Fartaria, Palo Alto, CA (US); Simon Fluckiger, Lenzburg (CH); Benjamin M. Haas, Roggwil (CH); and Fernando Franco, Zurich (CH)
Assigned to Siemens Healthineers International AG, Steinhausen (CH)
Filed by VARIAN MEDICAL SYSTEMS, INC., Palo Alto, CA (US)
Filed on Sep. 28, 2021, as Appl. No. 17/488,208.
Prior Publication US 2023/0097224 A1, Mar. 30, 2023
Int. Cl. G06T 7/00 (2017.01); G06N 3/045 (2023.01); G06T 7/11 (2017.01); G06T 7/13 (2017.01); G16H 30/40 (2018.01)
CPC G06T 7/0012 (2013.01) [G06N 3/045 (2023.01); G06T 7/11 (2017.01); G06T 7/13 (2017.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01)] 18 Claims
OG exemplary drawing
 
1. A method comprising:
executing, by a processor, a machine learning model that receives an input of a first image of an anatomical region of a patient depicting a first organ having an outline to cause the machine learning model to predict boundary information of the anatomical region within a reference image;
selecting, by the processor, a computer model from a library of computer models based on the boundary information of the anatomical region, the computer model configured to execute a contouring protocol identifying outlines of organs within anatomical regions; and
transmitting, by the processor, the boundary information of the anatomical region of the first image to the computer model based on selecting the computer model from the library of computer models, whereby the computer model executes the contouring protocol to identify the outline of the first organ within the anatomical region.