US 12,136,220 B2
Method and system for providing an at least 3-dimensional medical image segmentation of a structure of an internal organ
Aleksei Vasilev, Munich (DE); and Julian Praceus, Munich (DE)
Assigned to Laralab GmbH, Munich (DE)
Appl. No. 17/298,090
Filed by LARALAB GmbH, Munich (DE)
PCT Filed Dec. 2, 2019, PCT No. PCT/EP2019/083364
§ 371(c)(1), (2) Date May 28, 2021,
PCT Pub. No. WO2020/109630, PCT Pub. Date Jun. 4, 2020.
Claims priority of application No. 18209550 (EP), filed on Nov. 30, 2018.
Prior Publication US 2022/0028085 A1, Jan. 27, 2022
Int. Cl. G06T 7/11 (2017.01); G06N 3/08 (2023.01); G16H 30/40 (2018.01)
CPC G06T 7/11 (2017.01) [G06N 3/08 (2013.01); G16H 30/40 (2018.01); G06T 2207/10076 (2013.01); G06T 2207/10081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30048 (2013.01)] 21 Claims
OG exemplary drawing
 
1. A computer-implemented method for generating a 4-dimensional medical image segmentation for at least one structure of a human heart, comprising the steps of:
providing a first 4-dimensional medical image comprising the at least one structure of the human heart, the medical image being based on a computed tomography scan image;
generating a segmentation of at least part of the provided first 4-dimensional medical image using at least one first trained artificial neural network, wherein the at least one first trained artificial neural network is configured as a convolutional processing network with U-net architecture, wherein each convolutional processing network with U-net architecture comprises:
a down-sampling path comprising at least two processing convolutional blocks and at least two down-sampling blocks;
an up-sampling path comprising at least two processing convolutional blocks and at least two up-sampling blocks;
wherein the down-sampling path generates a direct input and/or an indirect input for the up-sampling path;
generating at least one 4-dimensional medical image segmentation for the at least one structure of the human heart based at least on the segmentation generated by the at least one first trained artificial neural network;
providing a computed tomography scan image as a second 4-dimensional medical image;
generating a segmentation for at least part of the second 4-dimensional medical image using a second trained artificial neural network configured as a convolutional processing network with U-net architecture,
determining a portion of the segmentation for the second 4-dimensional medical image which comprises the at least one structure of the human heart;
extracting a portion of the provided second 4-dimensional medical image corresponding to the determined portion of the segmentation for the second 4-dimensional medical image; and
providing the extracted portion as the first 4-dimensional medical image to the at least one first trained artificial neural network.