Classifying views of an angiographic medical imaging system
Systems and methods are provided for acquiring a series of angiographic images and identifying the anatomical structures represented in the series of images using a machine learnt classifier. Additional series of images that would yield the optimal visualization of the structure of interest may be suggested.
1. A method for classifying a video in coronary angiography, the method comprising:
acquiring a temporal series of angiographic image frames;
removing one or more angiographic image frames from the temporal series of angiographic image frames;
classifying by a machine-learnt classifier each of the remaining angiographic image frames of the temporal series of angiographic image frames as visualizing a first anatomical structure or a second anatomical structure;
labeling the temporal series of angiographic image frames as visualizing the first anatomical structure or the second anatomical structure based on the classifications of the remaining images; and
providing the label of the temporal series of angiographic image frames to a user.
2. The method of claim 1 , wherein the first anatomical structure is a left coronary artery and the second anatomical structure is a right coronary artery.
3. The method of claim 1 , wherein acquiring the temporal series of angiographic image frames is performed by an x-ray imaging device.
4. The method of claim 1 , wherein the one or more angiographic image frames are removed based on a contrast level.
5. The method of claim 4 , wherein removing comprises:
denoising an image frame of the angiographic image frames;
generating a binary image of the denoised image frame; and
removing the binary denoised image frame when a contrast level does not reach a predefined threshold.
6. The method of claim 1 , wherein removing comprises:
removing a first percentage of angiographic image frames from a start of the temporal series and a second percentage of angiographic image frames from an end of the temporal series.
7. The method of claim 1 , wherein classifying by a machine learnt classifier comprises:
training a convolutional neural network with training data comprising labeled prior image frames;
inputting each of the remaining images frames into the convolutional neural network; and
returning a classification from the convolutional neural network.
8. The method of claim 1 , wherein labeling comprises:
comparing a number of angiographic image frames classified as the first anatomical structure versus the second anatomical structure; and
selecting the first anatomical structure or the second anatomical structure based on the comparison.
9. The method of claim 1 , further comprising:
generating the optimality score for the temporal series of angiographic image frames as a function of:
s =(( a−n/ 2)*2/ n )2 wherein:
(a)=a number of angiographic frames classified as the first anatomical structure;
(n)=a total number of angiographic frames;
(s)=the optimality score; and
providing the optimality score to the user.
10. A method for classifying views in angiography, the method comprising:
acquiring a temporal series of angiographic images;
classifying, using a deep machine-learnt classifier, a view of an anatomical structure in each of the angiographic images of the temporal series of angiographic images;
scoring the temporal series of angiographic images based on the classifications of the view in each of the angiographic images;
identifying, based on the score, a second view of the anatomical structure, wherein the second view comprises one or more scan parameters for acquiring the second view; and
acquiring a second set of angiographic images with the one or more scan parameters.
11. The method of claim 10 , wherein the view is classified as either a view of a left coronary artery or a view of a right coronary artery.
12. The method of claim 11 , wherein scoring comprises:
generating the score for the temporal series as a function of:
s =(( a−n/ 2)*2/ n )2 wherein:
(a)=a number of angiographic images classified as the view of the left coronary artery;
(n)=a total number of angiographic images; and
(s)=the score.
13. The method of claim 10 , further comprising:
removing, prior to classifying, one or more angiographic images from the temporal series of angiographic images as a function of a contrast level in the one or more angiographic images.
14. The method of claim 10 , further comprising:
identifying, when acquiring the temporal series, an angle of a C-arm; wherein identifying the second view is further based on the angle.
15. A system for classifying views in angiography, the system comprising:
a medical image scanner configured to acquire a temporal series of angiographic images;
a processor configured to classify each image of the temporal series of angiographic images as visualizing an anatomical structure using a machine-learnt classifier, the processor further configured to determine a label for the temporal series of angiographic images based on the classifications of each of the images; and
a display configured to display the temporal series of angiographic images and the label.
16. The system of claim 15 , wherein each of the images are classified as either a view of a left coronary artery or a view of a right coronary artery.
17. The system of claim 15 , wherein the processor is configured to classify each of the images using a convolutional neural network.
18. The system of claim 17 , wherein the convolutional neural network is trained using image data and classification data from prior labeled images.
19. The system of claim 17 , wherein the processor is configured to classify each of the images using two or more convolutional neural networks.
20. The system of claim 15 , wherein the processor is further configured to calculate an optimality score for the temporal series based on the classifications.