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Automatic left ventricular segmentation in 4D interventional ultrasound data using a patient-specific temporal synchronized shape prior
Author(s): Pedro Morais; Sandro Queirós; Carla Pereira; António H. J. Moreira; Maria J. Baptista; Nuno F. Rodrigues; Jan D'hooge; Daniel Barbosa; João L. Vilaça
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Paper Abstract

The fusion of pre-operative 3D magnetic resonance (MR) images with real-time 3D ultrasound (US) images can be the most beneficial way to guide minimally invasive cardiovascular interventions without radiation. Previously, we addressed this topic through a strategy to segment the left ventricle (LV) on interventional 3D US data using a personalized shape prior obtained from a pre-operative MR scan. Nevertheless, this approach was semi-automatic, requiring a manual alignment between US and MR image coordinate systems. In this paper, we present a novel solution to automate the abovementioned pipeline. In this sense, a method to automatically detect the right ventricular (RV) insertion point on the US data was developed, which is subsequently combined with pre-operative annotations of the RV position in the MR volume, therefore allowing an automatic alignment of their coordinate systems. Moreover, a novel strategy to ensure a correct temporal synchronization of the US and MR models is applied. Finally, a full evaluation of the proposed automatic pipeline is performed. The proposed automatic framework was tested in a clinical database with 24 patients containing both MR and US scans. A similar performance between the proposed and the previous semi-automatic version was found in terms of relevant clinical measurements. Additionally, the automatic strategy to detect the RV insertion point showed its effectiveness, with a good agreement against manually identified landmarks. The proposed automatic method showed high feasibility and a performance similar to the semi-automatic version, reinforcing its potential for normal clinical routine.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491F (15 March 2019); doi: 10.1117/12.2512759
Show Author Affiliations
Pedro Morais, 2Ai - Polytechnic Institute of Cávado (Portugal)
Univ. do Minho (Portugal)
KU Leuven Univ. (Belgium)
Sandro Queirós, 2Ai - Polytechnic Institute of Cávado (Portugal)
Univ. do Minho (Portugal)
KU Leuven Univ. (Belgium)
Carla Pereira, Univ. do Minho (Portugal)
ICVS/3B's - PT Government Associate Lab. (Portugal)
António H. J. Moreira, Univ. do Minho (Portugal)
Maria J. Baptista, Univ. do Minho (Portugal)
ICVS/3B's - PT Government Associate Lab. (Portugal)
Nuno F. Rodrigues, Univ. do Minho (Portugal)
Jan D'hooge, KU Leuven (Belgium)
Daniel Barbosa, Univ. do Minho (Portugal)
João L. Vilaça, Instituto Politécnico do Cávado e do Ave (Portugal)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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