Apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy with an overlay
An apparatus and method for generating a three-dimensional (3D) model of cardiac anatomy including an overlay. The apparatus includes at least a processor configured receive a set of images of a cardiac anatomy pertaining to a subject, generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters includes receiving cardiac geometry training data including a plurality of image sets as input correlated to a plurality of shape parameter sets as output, training a shape identification model using the cardiac geometry training data, and generating the set of shape parameters using the shape identification model, generate a 3D model of the cardiac anatomy based on the set of shape parameters, generate a map by determine a level of uncertainty at each location of a plurality of locations on the generated 3D model, and overlay the map onto the 3D model.
1 . An apparatus for generating a three-dimensional (3D) model of cardiac anatomy with an overlay, wherein the apparatus comprises:
at least a processor; and
a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to:
receive a set of images of a cardiac anatomy pertaining to a subject, wherein receiving the set of images of the cardiac anatomy comprises extracting the set of images of the cardiac anatomy from a patient profile;
generate a set of shape parameters based on the set of images, wherein generating the set of shape parameters comprises generating the set of shape parameters as a function of the set of images and a shape identification model;
generate a 3D model of the cardiac anatomy based on the set of shape parameters, wherein generating the 3D model includes transforming the 3D model as a function of a plurality of mode changers within a statistical shape model;
generate a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model, wherein the map comprises a color-coded heatmap based on one or more levels of uncertainty, wherein each level of the one or more levels of uncertainty is assigned to at least an uncertainty category comprising a pixel-wise uncertainty associated with individual pixels in at least one image of the set of images; and
overlay the map onto the 3D model.
2 . The apparatus of claim 1 , wherein generating the set of shape parameters further comprises:
inputting the set of images into the shape identification model; and
wherein the shape identification model has been trained using cardiac geometry training data comprising a plurality of image sets as input correlated to a plurality of shape parameter sets as output.
3 . The apparatus of claim 2 , wherein generating the set of shape parameters further comprises:
receiving the cardiac geometry training data comprising the plurality of image sets as input correlated to the plurality of shape parameter sets as output; and
training the shape identification model using the cardiac geometry training data.
4 . The apparatus of claim 3 , wherein receiving the cardiac geometry training data comprises generating the cardiac geometry training data using a synthetic intracardiac echocardiography (ICE) data generator.
5 . The apparatus of claim 1 , wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the cardiac anatomy.
6 . The apparatus of claim 1 , wherein each shape parameter within the set of shape parameters comprises a corresponding parameter range.
7 . The apparatus of claim 1 , wherein receiving the set of images comprises receiving the set of images from the patient profile.
8 . The apparatus of claim 1 , wherein the instructions further configured to the at least a processor to overlay the map onto the 3D model.
9 . The apparatus of claim 8 , wherein the map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model.
10 . The apparatus of claim 8 , wherein overlaying the 3D model with the map comprises utilizing interactive visualization techniques configured to allow user-mediated augmentation of the set of images of cardiac anatomy.
11 . The apparatus of claim 1 , wherein generating the 3D model further comprises generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained.
12 . The apparatus of claim 1 , wherein overlaying the map onto the 3D model comprises overlaying an ICE frame to a corresponding location of the 3D model.
13 . A method for generating a three-dimensional (3D) model of cardiac anatomy with an overlay, wherein the method comprises:
receiving, by a processor, a set of images of a cardiac anatomy pertaining to a subject, wherein receiving the set of images of the cardiac anatomy comprises extracting the set of images of the cardiac anatomy from a patient profile;
generating, by the processor, a set of shape parameters based on the set of images, wherein generating the set of shape parameters comprises:
generating the set of shape parameters using the set of images and a shape identification model;
generating, by the processor, a 3D model of the cardiac anatomy based on the set of shape parameters generate a 3D model of the cardiac anatomy based on the set of shape parameters, wherein generating the 3D model includes transforming the 3D model as a function of a plurality of mode changers within a statistical shape model;
generating, by the processor, a map by determining a level of uncertainty at each location of a plurality of locations on the generated 3D model, wherein the map comprises a color-coded heatmap based on one or more levels of uncertainty, wherein each level of the one or more levels of uncertainty is assigned to at least an uncertainty category comprising a pixel-wise uncertainty associated with individual pixels in at least one image of the set of images; and
overlaying, by the processor, the map onto the 3D model.
14 . The method of claim 13 , wherein generating the set of shape parameters further comprises:
inputting the set of images into the shape identification model; and
wherein the shape identification model has been trained using cardiac geometry training data comprising a plurality of image sets as input correlated to a plurality of shape parameter sets as output.
15 . The method of claim 14 , wherein generating the set of shape parameters further comprises:
receiving the cardiac geometry training data comprising the plurality of image sets as input correlated to the plurality of shape parameter sets as output; and
training the shape identification model using the cardiac geometry training data.
16 . The method of claim 15 , wherein receiving the cardiac geometry training data comprises generating the cardiac geometry training data using a synthetic intracardiac echocardiography (ICE) data generator.
17 . The method of claim 13 , wherein the set of shape parameters comprises a plurality of numerical descriptors representing at least a geometric characteristic of the cardiac anatomy.
18 . The method of claim 13 , wherein each shape parameter within the set of shape parameters comprises a corresponding parameter range.
19 . The method of claim 13 , wherein receiving the set of images comprises receiving the set of images from the patient profile.
20 . The method of claim 13 , further comprising overlay, using the at least a processor, the map onto the 3D model.
21 . The method of claim 20 , wherein the map comprises a color-coded heat map configured to visualize one or more areas of uncertainty on the 3D model.
22 . The method of claim 20 , wherein overlaying the 3D model with the map comprises utilizing interactive visualization techniques configured to allow user-mediated augmentation of the set of images of cardiac anatomy.
23 . The method of claim 13 , wherein generating the 3D model further comprises generating a second 3D model as a function of the 3D model, by varying the set of shape parameters, wherein the second 3D model is statistically constrained.
24 . The method of claim 13 , wherein overlaying the 3D model comprises overlaying an ICE frame to a corresponding location of the 3D model.