US 12,293,524 B2
Predicting correctness of algorithmic segmentation
Alexandra Groth, Hamburg (DE); and Rolf Jurgen Weese, Norderstedt (DE)
Assigned to KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
Appl. No. 17/287,142
Filed by KONINKLIJKE PHILIPS N.V., Eindhoven (NL)
PCT Filed Oct. 14, 2019, PCT No. PCT/EP2019/077684
§ 371(c)(1), (2) Date Apr. 21, 2021,
PCT Pub. No. WO2020/083676, PCT Pub. Date Apr. 30, 2020.
Claims priority of application No. 18201798 (EP), filed on Oct. 22, 2018.
Prior Publication US 2021/0390707 A1, Dec. 16, 2021
Int. Cl. G06T 7/12 (2017.01); G06N 5/022 (2023.01); G16H 30/40 (2018.01)
CPC G06T 7/12 (2017.01) [G06N 5/022 (2013.01); G16H 30/40 (2018.01); G06T 2207/20081 (2013.01); G06T 2207/30048 (2013.01)] 14 Claims
OG exemplary drawing
 
1. A system for generating a prediction model for predicting a correctness of segmentation by a segmentation algorithm, the system comprising:
a data interface for accessing:
training image data; and
labels for respective image portions of the training image data, wherein the labels are obtained by applying the segmentation algorithm to the training image data, wherein the labels are indicative of a correctness of the segmentation of a boundary in the respective image portion by the segmentation algorithm; and
a processor subsystem configured to:
train the prediction model using a machine learning technique and using said labelled image portions as input; and
output model data representing the trained prediction model, wherein the processor subsystem is further configured to generate the labels for the respective image portions by:
via the data interface, accessing:
first segmentation data representing the segmentation of the boundary in the respective image portions by the segmentation algorithm;
second segmentation data representing a corrected segmentation of the boundary in the respective image portions by a user;
determining the correctness of segmentation by comparing the first segmentation data with the second segmentation data; and
assigning the labels which are indicative of the correctness of segmentation to the respective image portions.