MANET: Mitral Annulus Point Tracking Network in Cardiac Magnetic Resonance

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MANET: Mitral Annulus Point Tracking Network in Cardiac Magnetic Resonance
Title:
MANET: Mitral Annulus Point Tracking Network in Cardiac Magnetic Resonance
Other Titles:
2022 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
18 October 2022
Citation:
Chen, J., Yang, X., Leng, S., Tan, R.-S., Zeng, Z., & Zhong, L. (2022). MANET: Mitral Annulus Point Tracking Network in Cardiac Magnetic Resonance. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9898005
Abstract:
Cardiac magnetic resonance (CMR) imaging is frequently recommended for patients at intermediate risk of cardiovascular disease to triage them for medication or invasive aggressive treatment. Mitral annulus (MA) motion and velocities represent the cardiac contraction and relaxation, and hold potential to improve the detection of subtle cardiac dysfunction. However, conventional interpretation of CMR images requires expert manipulation and is often operator-dependent. In this paper, we propose an end-to-end MA Point Tracking Network (MANet) to automatically detect and track MA motion during cardiac cycle. The MANet model consists of MA point detection module and motion tracking module. In MA point detection, we design the convolutional-based feature extraction and elastic regression to detect MA points frame by frame of each CMR video. Then, in MA tracking, we adopt the Deep SORT model to capture spatio-temporal continuity between frames and fine-tune the coordinate position of MA points. 171 CMR videos with 4275 frames are used in comparison experiments, and the results demonstrate that our MANet model achieves promising performance in reference to clinical ground truth (r=0.71, P<$0.001). This work provides an important preamble for cardiac motion tracking and cardiac function evaluation.
License type:
Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
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