US 12,293,569 B2
System and method for generating training images
Seymour Francis Knowles-Barley, Wellington (NZ); and Ralph Highnam, Wellington (NZ)
Assigned to Volpara Health Technologies Limited, Wellington (NZ)
Appl. No. 17/782,162
Filed by VOLPARA HEALTH TECHNOLOGIES LIMITED, Wellington (NZ)
PCT Filed Dec. 2, 2020, PCT No. PCT/IB2020/061379
§ 371(c)(1), (2) Date Jun. 2, 2022,
PCT Pub. No. WO2021/111326, PCT Pub. Date Jun. 10, 2021.
Claims priority of application No. 1917578 (GB), filed on Dec. 2, 2019.
Prior Publication US 2023/0017138 A1, Jan. 19, 2023
Int. Cl. G06V 10/774 (2022.01); G06T 7/00 (2017.01); G06T 7/20 (2017.01)
CPC G06V 10/774 (2022.01) [G06T 7/0016 (2013.01); G06T 7/20 (2013.01); G06T 2207/20081 (2013.01)] 19 Claims
OG exemplary drawing
 
1. A method of training a machine learning model to recognize movement of a body part in an acquired medical image by modifying a blur convolution kernel constructed with pixels oriented in a direction of the movement; the method comprising:
determining at least one motion weighting factor corresponding to a motion time period when the body part is moving during acquisition of the medical image, and using the motion weighting factor to vary/modify the blur convolution kernel, wherein
at least one motion weighting factor corresponds to a first motion time period when the body part is accelerating from a first speed to a second speed, and scaling a kernel value of a pixel associated with the time period immediately before the first motion period to be greater than a kernel value of a pixel associated with the first motion time period.