Objective: We propose a novel approach to predict the Abdominal Aortic Aneurysm (AAA) growth in future time, using longitudinal computer tomography (CT) scans of AAAs that are captured at different times in a patient-specific way. Methods: We adopt a formulation that considers a surface of the AAA as a manifold embedded in a scalar field over the three dimensional (3D) space. For this formulation, we develop our Dynamical Gaussian Process Implicit Surface (DGPIS) model based on observed surfaces of 3D AAAs as visible variables while the scalar fields are hidden. In particular, we use Gaussian process regression to construct the field as an observation model from CT training image data. We then learn a dynamic model to represent the evolution of the field. Finally, we derive the predicted AAA surface from the predicted field along with uncertainty quantified in future time. Results: A dataset of 7 subjects (4-7 scans) was collected and used to evaluate the proposed method by comparing its prediction Hausdorff distance errors against those of simple extrapolation. In addition, we evaluate the prediction results with respect to a conventional shape analysis technique such as Principal Component Analysis (PCA). All comparative results show the superior prediction performance of the proposed approach. Conclusion: We introduce a novel approach to predict the AAA growth and its predicted uncertainty in future time, using longitudinal CT scans in a patient-specific fashion. Significance: The capability to predict the AAA shape and its confidence region by our approach establish the potential for guiding clinicians with informed decision in conducting medical treatment and monitoring of AAAs.
Bibliographical noteFunding Information:
Manuscript received December 23, 2017; revised May 12, 2018; accepted June 14, 2018. Date of publication July 2, 2018; date of current version February 18, 2019. This work was supported in part by the National Heart, Lung, and Blood Institute of the National Institutes of Health R01HL115185 and R21HL113857, and in part by National Science Foundation CAREER Award CMMI-1150376. The work of H. N. Do was supported by the Vietnam Education Foundation. The work of Jongeun Choi was supported in part by the National Research Foundation of Korea (NRF) under Grant 2018R1A4A1025986 funded by the Korea government (MSIT). (Corresponding author: Jonguen Choi.) H. N. Do is with the School of Computer Science University of Adelaide South Australia. A. Ijaz is with the ADDO AI.
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All Science Journal Classification (ASJC) codes
- Biomedical Engineering