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15 March 2019 Automatic left ventricular segmentation in 4D interventional ultrasound data using a patient-specific temporal synchronized shape prior
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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.
Conference Presentation
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Pedro Morais, Sandro Queirós, Carla Pereira, António H. J. Moreira, Maria J. Baptista, Nuno F. Rodrigues, Jan D'hooge, Daniel Barbosa, and João L. Vilaça "Automatic left ventricular segmentation in 4D interventional ultrasound data using a patient-specific temporal synchronized shape prior", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109491F (15 March 2019); doi: 10.1117/12.2512759; https://doi.org/10.1117/12.2512759
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