Paper
20 March 2015 Improving the robustness of interventional 4D ultrasound segmentation through the use of personalized prior shape models
Daniel Barbosa, Sandro Queirós, Pedro Morais, Maria J. Baptista, Mark Monaghan, Nuno F. Rodrigues, Jan D'hooge, João L. Vilaça
Author Affiliations +
Abstract
While fluoroscopy is still the most widely used imaging modality to guide cardiac interventions, the fusion of pre-operative Magnetic Resonance Imaging (MRI) with real-time intra-operative ultrasound (US) is rapidly gaining clinical acceptance as a viable, radiation-free alternative. In order to improve the detection of the left ventricular (LV) surface in 4D ultrasound, we propose to take advantage of the pre-operative MRI scans to extract a realistic geometrical model representing the patients cardiac anatomy. This could serve as prior information in the interventional setting, allowing to increase the accuracy of the anatomy extraction step in US data. We have made use of a real-time 3D segmentation framework used in the recent past to solve the LV segmentation problem in MR and US data independently and we take advantage of this common link to introduce the prior information as a soft penalty term in the ultrasound segmentation algorithm. We tested the proposed algorithm in a clinical dataset of 38 patients undergoing both MR and US scans. The introduction of the personalized shape prior improves the accuracy and robustness of the LV segmentation, as supported by the error reduction when compared to core lab manual segmentation of the same US sequences.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Daniel Barbosa, Sandro Queirós, Pedro Morais, Maria J. Baptista, Mark Monaghan, Nuno F. Rodrigues, Jan D'hooge, and João L. Vilaça "Improving the robustness of interventional 4D ultrasound segmentation through the use of personalized prior shape models", Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 94133H (20 March 2015); https://doi.org/10.1117/12.2081813
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KEYWORDS
Magnetic resonance imaging

Image segmentation

Ultrasonography

Algorithm development

Fluoroscopy

Image processing algorithms and systems

Shape analysis

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