Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

Wenyao Xia; John Moore; Elvis C. S. Chen; Yuanwei Xu; Olivia Ginty; Daniel Bainbridge; Terry M. Peters

doi:10.1117/1.JMI.5.2.021214

9 February 2018 Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling

Wenyao Xia, John Moore, Elvis C. S. Chen, Yuanwei Xu, Olivia Ginty, Daniel Bainbridge, Terry M. Peters

Author Affiliations +

Journal of Medical Imaging, Vol. 5, Issue 2, 021214 (February 2018). https://doi.org/10.1117/1.JMI.5.2.021214

Abstract

Three-dimensional ultrasound segmentation of mitral valve (MV) at diastole is helpful for duplicating geometry and pathology in a patient-specific dynamic phantom. The major challenge is the signal dropout at leaflet regions in transesophageal echocardiography image data. Conventional segmentation approaches suffer from missing sonographic data leading to inaccurate MV modeling at leaflet regions. This paper proposes a signal dropout correction-based ultrasound segmentation method for diastolic MV modeling. The proposed method combines signal dropout correction, image fusion, continuous max-flow segmentation, and active contour segmentation techniques. The signal dropout correction approach is developed to recover the missing segmentation information. Once the signal dropout regions of TEE image data are recovered, the MV model can be accurately duplicated. Compared with other methods in current literature, the proposed algorithm exhibits lower computational cost. The experimental results show that the proposed algorithm gives competitive results for diastolic MV modeling compared with conventional segmentation algorithms, evaluated in terms of accuracy and efficiency.

Citation Download Citation

Wenyao Xia, John Moore, Elvis C. S. Chen, Yuanwei Xu, Olivia Ginty, Daniel Bainbridge, and Terry M. Peters "Signal dropout correction-based ultrasound segmentation for diastolic mitral valve modeling," Journal of Medical Imaging 5(2), 021214 (9 February 2018). https://doi.org/10.1117/1.JMI.5.2.021214

Received: 28 August 2017; Accepted: 4 January 2018; Published: 9 February 2018

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