Image segmentation using neural network method
The present disclosure relates to systems, methods, devices, and non-transitory computer-readable storage medium for segmenting three-dimensional images. In one implementation, a computer-implemented method for segmenting a three-dimensional image is provided. The method may include receiving a three-dimensional image acquired by an imaging device, and selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional image. The method may further include segmenting, by a processor, each stack of adjacent two-dimensional images using a neural network model. The method may also include determining, by the processor, a label map for the three-dimensional image by aggregating the segmentation results from the plurality of stacks.
1. A computer-implemented method for segmenting a three-dimensional medical image, the method comprising:
receiving the three-dimensional medical image acquired by an imaging device;
selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional medical image;
segmenting, by a processor, each stack of adjacent two-dimensional images using a neural network model; and
determining, by the processor, a label map for the three-dimensional medical image by aggregating the segmentation results from the plurality of stacks.
2. The method of claim 1 , further including training the neural network model using at least one three-dimensional medical training image.
3. The method of claim 2 , wherein training the neural network model includes determining parameters of at least one convolution filter used in the neural network model.
4. The method of claim 1 , wherein each stack includes an odd number of two-dimensional images, and wherein segmenting the stack of adjacent two-dimensional images includes determining a label map for the two-dimensional image in the middle of the stack.
5. The method of claim 1 , wherein each stack includes an even number of two-dimensional images, and wherein segmenting the stack of adjacent two-dimensional images includes determining a label map for at least one of the two two-dimensional images in the middle of the stack.
6. The method of claim 1 , wherein the adjacent two-dimensional images are in the same plane and carry dependent structure information in an axis orthogonal to the plane.
7. The method of claim 1 , wherein the neural network model is a deep convolutional neural network model.
8. The method of claim 1 , wherein the three-dimensional medical image is a medical image indicative of anatomical structures of a patient, wherein the label map associates an anatomic structure to each voxel of the three-dimensional medical image.
9. A device for segmenting a three-dimensional medical image, the device comprising:
an input interface that receives the three-dimensional medical image acquired by an imaging device;
at least one storage device configured to store the three-dimensional medical image; and
an image processor configured to:
select a plurality of stacks of adjacent two-dimensional images from the three-dimensional medical image;
segment each stack of adjacent two-dimensional images using a neural network model; and
determine a label map for the three-dimensional medical image by aggregating the segmentation results from the plurality of stacks.
10. The device of claim 9 , wherein the image processor is further configured to train the neural network model using at least one three-dimensional medical training image.
11. The device of claim 10 , wherein the image processor is further configured to determine parameters of at least one convolution filter used in the neural network model.
12. The device of claim 9 , wherein each stack includes an odd number of two-dimensional images, and wherein the image processor is further configured to determine a label map for the two-dimensional image in the middle of the stack.
13. The device of claim 9 , wherein each stack includes an even number of two-dimensional images, and wherein the image processor is further configured to determine a label map for at least one of the two two-dimensional image in the middle of the stack.
14. The device of claim 9 , wherein the adjacent two-dimensional images are in the same plane and carry dependent structure information in an axis orthogonal to the plane.
15. The device of claim 9 , wherein the neural network model is a deep convolutional neural network model.
16. The device of claim 9 , wherein the three-dimensional medical image is a medical image indicative of anatomical structures of a patient, wherein the label map associates an anatomic structure to each voxel of the three-dimensional medical image.
17. A non-transitory computer-readable medium containing instructions that, when executable by at least one processor, cause the at least one processor to perform a method for segmenting a three-dimensional medical image, the method comprising:
receiving the three-dimensional medical image acquired by an imaging device;
selecting a plurality of stacks of adjacent two-dimensional images from the three-dimensional medical image;
segmenting each stack of adjacent two-dimensional images using a neural network model; and
determining a label map for the three-dimensional medical image by aggregating the segmentation results from the plurality of stacks.
18. The non-transitory computer-readable medium of claim 17 , wherein the method further includes training the neural network model using at least one three-dimensional medical training image.
19. The non-transitory computer-readable medium of claim 18 , wherein training the neural network model includes determining parameters of at least one convolution filter used in the neural network model.
20. The non-transitory computer-readable medium of claim 17 , wherein each stack includes an odd number of two-dimensional images, and wherein segmenting the stack of adjacent two-dimensional images includes determining a label map for the two-dimensional image in the middle of the stack.
21. The non-transitory computer-readable medium of claim 17 , wherein each stack includes an even number of two-dimensional images, and wherein segmenting the stack of adjacent two-dimensional images includes determining a label map for at least one of the two two-dimensional images in the middle of the stack.
22. The non-transitory computer-readable medium of claim 17 , wherein the adjacent two-dimensional images are in the same plane and carry dependent structure information in an axis orthogonal to the plane.
23. The non-transitory computer-readable medium of claim 17 , wherein the neural network model is a deep convolutional neural network model.
24. The non-transitory computer-readable medium of claim 17 , wherein the three-dimensional medical image is a medical image indicative of anatomical structures of a patient, wherein the label map associates an anatomic structure to each voxel of the three-dimensional medical image.