Granted Patent B2

US 12,322,103 · App. 18/721,224 · Granted Jun 3, 2025

Inventors: Asher Gringauz (Nesher, IL); Liran Goshen (Pardes-Hanna, IL)

Assignee: KONINKLIJKE PHILIPS N.V.

G06T7/0012G06T2200/04G06T2207/20084G06T2207/30004

Abstract

Claims (55)

1. A computer-implemented method for processing 3D image data using a neural network architecture, the computer-implemented method comprising:

obtaining input 3D image data;

performing a modified 3D image data generation process comprising:

for each of a plurality of 3D sub-volumes of the input 3D image data, processing the 3D sub-volume using a first neural network to produce first modified 3D image data; and

for each 2D image slice along a first dimension of the input 3D image data, processing the 2D image slice using a second neural network to produce second modified 3D image data; and

processing the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, using an outputting neural network to produce an output of the neural network architecture.

2. The computer-implemented method of claim 1 , wherein processing the input 3D image data, together with the second modified 3D image data and the first modified 3D image data, comprises:

for each 2D image slice along a second dimension of the input 3D image data, different to the first dimension:

obtaining a corresponding 2D image slice from the first modified 3D image data and the second modified 3D imaging data; and

processing the 2D image slice of the input 3D image data, together with at least the corresponding 2D image slice from the first modified 3D image data and the corresponding slice from the second modified 3D imaging data, using the outputting neural network to produce a processed 2D image slice,

wherein the combination of the processed 2D image slices forms the output of the neural network architecture.

3. The computer-implemented method of claim 2 , wherein the input 3D image data is a medical image of a subject, and each 2D image slice along the first dimension is a slice lying in a coronal or sagittal plane of the subject, and each 2D image slice along the second dimension is a slice lying in an axial plane of the subject.

4. The computer-implemented method of claim 2 , wherein:

the modified 3D image data generation process further comprises, for each 2D image slice along a third dimension of the input 3D image data, processing the 2D image slice along the third dimension using a third neural network to produce third modified 3D image data;

the third dimension is different to the first dimension; and

the processing of the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, comprises processing at least the input 3D image data, the second modified 3D image data and the third modified 3D image data using the outputting neural network to produce the output of the neural network architecture.

5. The computer-implemented method of claim 4 ,

wherein processing the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, comprises:

for each 2D image slice along the second dimension of the input 3D image data:

obtaining a corresponding 2D image slice from the second modified 3D image data;

obtaining a corresponding 2D image slice from the third modified 3D image data and

processing the 2D image slice of the input 3D image data, together at least with the corresponding 2D image slice from the second modified 3D image data and the corresponding slice from the third modified 3D imaging data, using the outputting neural network to produce a processed 2D image slice,

wherein the combination of the processed 2D image slices forms the output of the neural network architecture.

6. The computer-implemented method of claim 4 , wherein the processing the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, comprises processing the input 3D image data, the first modified 3D image data, the second modified 3D image data and the third modified 3D image data using the outputting neural network to produce the output of the neural network architecture.

7. The computer-implemented method of claim 6 ,

wherein processing the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, comprises:

for each 2D image slice along the second dimension of the input 3D image data:

obtaining a corresponding 2D image slice from the first modified 3D image data;

obtaining a corresponding 2D image slice from the second modified 3D image data;

obtaining a corresponding 2D image slice from the third modified 3D image data; and

processing the 2D image slice of the input 3D image data, together with at least the corresponding 2D image slice from the first modified 3D image data and the corresponding slice from the second modified 3D imaging data and the corresponding slice from the third modified 3D imaging data, using the outputting neural network to produce a processed 2D image slice,

wherein the combination of the processed 2D image slices forms the output of the neural network architecture.

8. The computer-implemented method of claim 4 , wherein:

the input 3D image data is a medical image of a subject;

each 2D image slice along the first dimension is a slice lying in a coronal plane of the subject;

each 2D image slice along the second dimension is a slice lying in an axial plane of the subject; and

each 2D image slice along the third dimension is a slice lying in a sagittal plane of the subject.

9. The computer-implemented method of claim 4 , wherein the second neural network and the third neural network are one and the same.

10. The computer-implemented method of claim 4 , wherein the second neural network and the third neural network are different.

11. The computer-implemented method of claim 1 , wherein each of the first, second and outputting neural networks have a U-net architecture.

12. The computer-implemented method of claim 1 , wherein the size of each of the plurality of sub-volumes is no greater than one sixteenth of the size of the input 3D image data.

13. A system for processing 3D image data using a neural network architecture, the system comprising:

a memory that stores a plurality of instructions; and

a processor coupled to the memory and configured to execute the plurality of instructions to:

obtain input 3D image data;

perform a modified 3D image data generation process comprising:

for each of a plurality of 3D sub-volumes of the input 3D image data, processing the 3D sub-volume using a first neural network to produce first modified 3D image data; and

for each 2D image slice along a first dimension of the input 3D image data, processing the 2D image slice using a second neural network to produce second modified 3D image data; and

process the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, using an outputting neural network to produce an output of the neural network architecture.

14. A non-transitory computer readable medium comprising executable instructions which, when executed by at least one processor, cause the at least one processor to perform a method for processing 3D image data using a neural network architecture, the method comprising:

obtaining input 3D image data;

performing a modified 3D image data generation process comprising:

for each of a plurality of 3D sub-volumes of the input 3D image data, processing the 3D sub-volume using a first neural network to produce first modified 3D image data; and

for each 2D image slice along a first dimension of the input 3D image data, processing the 2D image slice using a second neural network to produce second modified 3D image data; and

processing the input 3D image data, together with at least the first modified 3D image data and the second modified 3D image data, using an outputting neural network to produce an output of the neural network architecture.

Assignments (1)

Priority Claims (1)

Continuity (1)

References Cited (29)