Toward an automatic segmentation of mitral valve chordae
| Autor: | Daryna Panicheva, Marie-Odile Berger, Pierre-Frédéric Villard |
|---|---|
| Přispěvatelé: | Augmentation visuelle d'environnements complexes (MAGRIT-POST), Inria Nancy - Grand Est, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Department of Algorithms, Computation, Image and Geometry (LORIA - ALGO), Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Centre National de la Recherche Scientifique (CNRS), SPIE, Barjor Gimi and Andrzej Kro, Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS)-Université de Lorraine (UL) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Computer science
medicine.medical_treatment 02 engineering and technology Ellipse 01 natural sciences 010309 optics Mitral valve 0103 physical sciences medicine [INFO.INFO-IM]Computer Science [cs]/Medical Imaging Segmentation Computer vision Systole Mitral valve repair business.industry Perspective (graphical) 021001 nanoscience & nanotechnology Topology-based image analysis [INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation Finite element method medicine.anatomical_structure [INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] Line (geometry) segmentation methodologies Artificial intelligence 0210 nano-technology business |
| Zdroj: | SPIE Medical Imaging SPIE Medical Imaging, SPIE, Feb 2019, San Diego, United States. pp.1095315-1095323, ⟨10.1117/12.2511943⟩ Medical Imaging: Biomedical Applications in Molecular, Structural, and Functional Imaging |
| DOI: | 10.1117/12.2511943⟩ |
| Popis: | International audience; Heart disease is the leading cause of death in the developed world. Cardiac pathologies include abnormal closure of the mitral valve, which can be treated by surgical operations, but the repair outcome varies greatly based on the experience of the surgeon. Simulating the procedure with a computer-based tool can greatly improve valve repair. Various teams are working on biomechanical models to compute the valve behaviour during peak systole. Although they use an accurate finite element method, they also use a tedious manual segmentation of the valve. Providing means to automatically segment the chordae and the leaflets would allow significant progress in the perspective of simulating the surgical gesture for the mitral valve repair. Valve chordae are generalized cylinders: Instead of being limited to a line, the central axis is a continuous curve. Instead of a constant radius, the radius varies along the axis. In most of the cases chordae sections are flattened ellipses and classical model-based methods commonly used for vessel enhancement or vessel segmentation fail. In this paper, we exploit the fact that there are no other generalized cylinders than the chordae in the micro CT scan and we propose a topology-based method for the chordae extraction. This approach is flexible and only requires the knowledge of an upper bound of the maximum chordae radius. Examples of segmentation are provided on three porcine datasets. The reliability of the segmentation is proved with a dataset where the ground truth is available. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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