Deep learning volumetric deformable registration
AssigneeThe Regents of the University of California
InventorsAlbert Hsiao, Kyle Hasenstab
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Abstract
A method and system for automated deformable registration of an organ from medical images includes generating segmentations of the organ by processing a first and second series of images corresponding to different organ states using a first trained CNN. A second trained CNN processes the first and second series of images and the segmentations to deformably register the second series of images to the first series of images. The second trained CNN predicts a displacement field by minimizing a registration loss function, where the displacement field maximizes colocalization of the organ between the different states.
Claims14
A method for automated deformable registration of an organ from images, the method comprising:
- receiving in a computer processor configured for executing a trained convolutional neural network (CNN) image data to comprising computed tomography (CT) series pairs comprising a first series of images of the organ in a first state and a second series of images of the organ in at least one second state;
- propagating the first and second series of images of the organ using a first trained CNN to generate segmentations of the organ;
- propagating the first and second series of images and the segmentations of the organ using a second trained CNN to deformably register the second series of images to the first series of images, wherein the second trained CNN is trained to predict a displacement field by minimizing a registration loss function, wherein the displacement field is configured to maximize colocalization of the organ between the first state and the at least one second state;
- and
- generating an output comprising a deformed image corresponding to the at least one second state;
- wherein the organ comprises one or more lung, the first state is inspiratory and the at least one second state is expiratory, and the CT series pairs comprise volumetric lung CT scans.
A system for automated deformable registration of an organ from medical images, comprising:
- at least one computer processor configured to:
- acquire image data comprising computed tomography (CT) series pairs comprising a first series of images of the organ in a first state and a second series of images of the organ in at least one second state;
- execute a first convolutional neural network (CNN), wherein the first CNN trained to generate segmentations of the organ using the first and second series of images;
- execute a second CNN, wherein the second CNN is trained to deformably register the second series of images to the first series of images using the first and second series of images and the segmentations of the organ, wherein the second CNN is further configured to predict a displacement field by minimizing a registration loss function, wherein the displacement field is configured to maximize colocalization of the organ between the first state and the at least one second state;
- and
- generate an output comprising a deformed image corresponding to the at least one second state;
- wherein the organ comprises one or more lung, the first state is inspiratory and the at least one second state is expiratory, and the CT series pairs comprise volumetric lung CT scans.
Dependencies are inferred from the claim text.