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EP-4736114-A1 - SYSTEM AND METHOD FOR DETERMINING GEOMETRY OF ANATOMICAL FEATURES FROM MEDICAL IMAGES

EP4736114A1EP 4736114 A1EP4736114 A1EP 4736114A1EP-4736114-A1

Abstract

A three-dimensional (3D) image of a cardiac region is input to a convolutional neural network (CNN) to semantically segment the 3D image. An output of the CNN includes a 3D geometry of a heart valve structure within the cardiac region. A chain code is applied to the 3D geometry to obtain two-dimensional (2D) geometric parameters of the heart valve structure. The 2D geometric parameters are used to recommend or select an implantable device that fits with the heart valve structure.

Inventors

  • MAHENDRA, Nikhil
  • DUBHASHI, Abhijeet A.
  • SCHNEIDER-MARTIN, Jacob
  • TUNENDER, Madeline M.
  • Varahoor, Srinivasan S.
  • PAITEL, Yvan R.
  • PATIL, KAUSTUBH R.
  • ELLIOTT, Jeannine

Assignees

  • Medtronic, Inc.

Dates

Publication Date
20260506
Application Date
20240625

Claims (20)

  1. 1. A computer-implemented method comprising: obtaining a three-dimensional (3D) image of a cardiac region; inputting the 3D image into a convolutional neural network (CNN) to semantically segment the 3D image, an output of the CNN comprising a 3D geometry of a heart valve structure within the cardiac region; applying a chain code to the 3D geometry to obtain two-dimensional (2D) geometric parameters of the heart valve structure; and using the 2D geometric parameters to recommend or select an implantable device that fits with the heart valve structure.
  2. 2. The method of claim 1, wherein the heart valve structure comprises a mitral valve annulus, and wherein the implantable device comprises a transcatheter mitral valve replacement system.
  3. 3. The method of claim 2, wherein the 2D geometric parameters include the following parameters of the mitral valve annulus: an inter-commissural diameter, an anterior-posterior diameter, a perimeter, and an area.
  4. 4. The method of any previous method claim, wherein inputting the 3D image into the CNN comprises: inputting a 3D heart scan image into a first CNN that produces a bounding volume that contains the heart valve structure; bounding the 3D heart scan image by the bounding volume; and inputting the bounded image to a second CNN to obtain the 3D geometry of the heart valve structure.
  5. 5. The method of any previous method claim, further comprising training the CNN on patient data sets in which a clinician manually defines a 2D outline on a 3D heart image, the 2D outline defining at least one boundary of an equivalent heart valve structure.
  6. 6. The method of any previous method claim, wherein the 3D geometry of the heart valve structure has a volume that is less than 0. 1% of the 3D image.
  7. 7. The method of any previous method claim, wherein applying the chain code to the 3D geometry comprises projecting the 3D geometry to a plane, the chain code being applied in 2D to the projection.
  8. 8. The method of any previous method claim, wherein the 3D image is constructed from a time series of scanned 3D images.
  9. 9. The method of any previous method claim, wherein the 3D image comprises a computed tomography (CT) image.
  10. 10. The method of claim 9, wherein the CNN is trained on any combination of CT images, synthetic data generated by scanning phantoms, and different modality images obtained from imaging modalities other than CT, the different modality images translated to a CT input using one or more generative adversarial networks (GANs).
  11. 11. A non-transitory computer-readable medium comprising instructions that, when executed by a processor, perform any previous method claim.
  12. 12. A computer-implemented method comprising: obtaining a three-dimensional (3D) image of a cardiac region; inputting the 3D image into a convolutional neural network (CNN) to semantically segment the 3D image, an output of the CNN comprising a 3D geometry of a mitral valve annulus; applying a chain code to the 3D geometry to obtain two-dimensional (2D) geometric parameters of the mitral valve annulus; and using the 2D geometric parameters to recommend or select transcatheter mitral valve replacement that fits with the mitral valve annulus while minimizing risk of left ventricular outflow tract obstruction.
  13. 13. The method of claim 12, wherein the 2D geometric parameters include the following parameters of the mitral valve annulus: an inter-commissural diameter, an anterior-posterior diameter, a perimeter, and an area.
  14. 14. The method of claim 12, wherein inputting the 3D image into the CNN comprises: inputting a 3D heart scan image into a first CNN that produces a bounding volume that contains the mitral valve annulus; bounding the 3D heart scan image by the bounding volume; and inputting the bounded image to a second CNN to obtain the 3D geometry of the mitral valve annulus.
  15. 15. The method of claim 12, further comprising training the CNN on patient data sets in which 2D outlines are superimposed over mitral annuli on 3D heart images.
  16. 16. The method of claim 12, wherein the 3D geometry of the mitral annulus has a volume that is less than 0.1% of the 3D image.
  17. 17. The method of claim 12, wherein applying the chain code to the 3D geometry comprises projecting the 3D geometry to a plane, the chain code being applied in 2D to the projection.
  18. 18. The method of claim 12, wherein the 3D image is constructed from a time series of scanned 3D images.
  19. 19. The method of claim 12, wherein the 3D image comprises a computed tomography (CT) image, and wherein the CNN is trained on any combination of CT images, synthetic data generated by scanning phantoms, and different modality images obtained from imaging modalities other than CT, the different modality images translated to a CT input using one or more generative adversarial networks (GANs).
  20. 20. A non-transitory computer-readable medium comprising instructions that, when executed by a processor, perform the method of claim 12.

Description

SYSTEM AND METHOD FOR DETERMINING GEOMETRY OF ANATOMICAL FEATURES FROM MEDICAL IMAGES CROSS-REFERENCE TO RELATED APPLICATION [0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63/523,402, filed June 27, 2023, the entire content of which is incorporated herein by reference. FIELD [0002] The present technology is generally related to machine-leaming-based analysis of three-dimensional medical images. BACKGROUND [0003] Medical imaging is an instrumental tool in medical diagnosis and treatment. Three-dimensional (3D) medical imaging has gained widespread adoption in the medical field. Three-dimensional medical imaging provides a detailed and accurate visualization of internal structures and tissues, which can be used to diagnose and treat complex medical conditions. [0004] Three-dimensional medical imaging is achieved through a variety of imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound. These techniques create three-dimensional images of organs, bones, tissues, and other structures in the body, providing practitioners with a detailed and accurate representation of the patient's internal anatomy. With the ability to view the body from multiple angles, medical professionals can make more precise diagnoses, plan complex surgical procedures, and improve patient outcomes. [0005] It can sometimes be difficult to find features (e.g., anatomical structures) in a 3D image. The image data is typically stored in a 3D array which may be viewed on as a 3D image on a 2D display, and the structures of interest can sometimes be obscured by other anatomical features making them hard to locate in in 3D renderings, or even in smaller views (e.g., sub-arrays of the image structure). Viewing the image as two- dimensional (2D) sections can bypass obscuring structures. However, because anatomical features are geometrically complex organic shapes that can differ significantly between individuals and will typically not be aligned with viewing planes, they can sometimes be hard to identify in 2D views. Because of the complexities involved in presenting and manipulating 3D images, some medical 3D imaging tasks can be time consuming to perform, resulting in significant costs being associated with those tasks. SUMMARY [0006] The present disclosure is directed to determining geometry of anatomical features from medical imaging. In one embodiment, a method involves obtaining a three- dimensional (3D) image of a cardiac region. The 3D image is input to a convolutional neural network (CNN) to semantically segment the 3D image. An output of the CNN includes a 3D geometry of a heart valve structure within the cardiac region. A chain code is applied to the 3D geometry to obtain two-dimensional (2D) geometric parameters of the heart valve structure. The 2D geometric parameters are used to recommend or select an implantable device that fits with the heart valve structure. [0007] In one embodiment, the heart valve structure includes a mitral valve annulus, and the implantable device comprises a transcatheter mitral valve replacement system. In such a case, the 2D geometric parameters may include the following parameters of the mitral valve annulus: an inter-commissural diameter, an anterior- posterior diameter, a perimeter, and an area. In one embodiment, inputting the 3D image into the CNN involves: inputting a 3D heart scan image into a first CNN that produces a bounding volume that contains the heart valve structure; bounding the 3D heart scan image by the bounding volume; and inputting the bounded image to a second CNN to obtain the 3D geometry of the heart valve structure. In another embodiment, the method further involves training the CNN on patient data sets in which a clinician manually defines a 2D outline on a 3D heart image. The 2D outline defines at least one boundary of an equivalent heart valve structure. In some embodiments, the 3D geometry of the heart valve structure has a volume that is less than 0. 1% of the 3D image. Applying the chain code to the 3D geometry may involve projecting the 3D geometry to a plane, the chain code being applied in 2D to the projection. The 3D image may include a 3D video image. [0008] These and other features and aspects of various embodiments may be understood in view of the following detailed discussion and accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS [0009] The discussion below makes reference to the following figures, wherein the same reference number may be used to identify the similar/same component in multiple figures. [0010] FIG. 1 is a diagram showing a heart structure analyzed using systems and methods according to an example embodiment; [0011] FIG. 2 is a medical image overlaid with geometric shapes which can be used fortraining a neural network according to example embodiments; [0012] FIG. 3 is a diagram showing a set of convolutional neural networks according to example embodimen