US 12,213,643 B2
Learning device and medical image processing device
Taihei Michihata, Tokyo (JP)
Assigned to Sony Olympus Medical Solutions Inc., Tokyo (JP)
Appl. No. 17/798,570
Filed by Sony Olympus Medical Solutions Inc., Tokyo (JP)
PCT Filed Feb. 9, 2021, PCT No. PCT/JP2021/004847
§ 371(c)(1), (2) Date Aug. 10, 2022,
PCT Pub. No. WO2021/166749, PCT Pub. Date Aug. 26, 2021.
Claims priority of application No. 2020-024944 (JP), filed on Feb. 18, 2020.
Prior Publication US 2023/0112628 A1, Apr. 13, 2023
Int. Cl. A61B 1/005 (2006.01); A61B 1/00 (2006.01); A61B 1/04 (2006.01); A61B 1/05 (2006.01); A61B 1/06 (2006.01); A61B 1/07 (2006.01)
CPC A61B 1/000096 (2022.02) [A61B 1/043 (2013.01); A61B 1/0638 (2013.01); A61B 1/0655 (2022.02)] 13 Claims
OG exemplary drawing
 
1. A learning device comprising:
circuitry configured to
acquire training images in which a first training image acquired by capturing light from a subject irradiated with light in a first wavelength band and a second training image acquired by capturing light from the subject irradiated with light in a second wavelength band different from the first wavelength band are paired;
specify a singular area in the second training image, wherein the singular area is an area in which a pixel level is equal to or larger than a specific threshold in the second training image, the singular area includes a first singular area in which the pixel level is within a first range each of a first singular area in which the pixel level is within a first range and a second singular area in which the pixel level is within a second range higher than the first range;
extract feature data of each of a first singular-corresponding area, which is the singular-corresponding area in the first training image and is at a pixel position corresponding to the first singular area, and a second singular-corresponding area, which is the singular-corresponding area in the first training image and is at a pixel position corresponding to the second singular area, specified in the second training image; and
generate a learning model by performing machine learning on the first singular-corresponding area and the second singular-corresponding area based on the feature data.