Abstract
We address the problem of cardiovascular shape representation from misaligned Cardiovascular Magnetic Resonance (CMR) images. An accurate 3D representation of the heart geometry allows for robust metrics to be calculated for multiple applications, from shape analysis in populations to precise description and quantification of individual anatomies including pathology. Clinical CMR relies on the acquisition of heart images at different breath holds potentially resulting in a misaligned stack of slices. Traditional methods for 3D reconstruction of the heart geometry typically rely on alignment, segmentation and reconstruction independently. We propose a novel method that integrates simultaneous alignment and segmentation refinements to realign slices producing a spatially consistent arrangement of the slices together with their segmentations fitted to the image data.
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Villard, B., Zacur, E., Grau, V. (2019). ISACHI: Integrated Segmentation and Alignment Correction for Heart Images. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_19
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DOI: https://doi.org/10.1007/978-3-030-12029-0_19
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