US 12,118,727 B2
System and method for training a machine learning model and for providing an estimated interior image of a patient
Sebastian Andersson, Stockholm (SE); Kjell Eriksson, Balsta (SE); Stina Svensson, Stockholm (SE); and Ola Weistrand, Huddinge (SE)
Assigned to Raysearch Laboratories AB (publ), Stockholm (SE)
Appl. No. 17/596,287
Filed by RaySearch Laboratories AB, Stockholm (SE)
PCT Filed Jun. 1, 2020, PCT No. PCT/EP2020/065117
§ 371(c)(1), (2) Date Dec. 7, 2021,
PCT Pub. No. WO2020/249414, PCT Pub. Date Dec. 17, 2020.
Claims priority of application No. 19180030 (EP), filed on Jun. 13, 2019.
Prior Publication US 2022/0230319 A1, Jul. 21, 2022
Int. Cl. G06T 7/00 (2017.01); G16H 30/40 (2018.01)
CPC G06T 7/0016 (2013.01) [G16H 30/40 (2018.01); G06T 2207/10076 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/10088 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30004 (2013.01)] 15 Claims
OG exemplary drawing
 
1. A computer-based method of training a deep learning model for providing an estimated image of an interior of a patient, based on a number of image sets, each image set comprising a first interior image of an interior of a person and a contour image of the person's outer contour at a specific point in time, and a second interior image of the interior of the person, comprising the steps of:
a. submitting the image sets to a deep learning model; and
b. training the model to establish an optimized parametrized conversion function G specifying the correlation between the interior of the person and the person's outer contour by, for each image set, applying the model to the contour image and the second interior image of the image set, comparing the output to the first interior image of the image set and using the result of the comparison to train the model.