H Martin Kjer1, Jens Fagertun2, Wilhelm Wimmer3, Nicolas Gerber3, Sergio Vera4, Livia Barazzetti5, Nerea Mangado6, Mario Ceresa6, Gemma Piella6, Thomas Stark7, Martin Stauber8, Mauricio Reyes5, Stefan Weber3, Marco Caversaccio9, Miguel Ángel González Ballester6,10, Rasmus R Paulsen2. 1. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark. hmkj@dtu.dk. 2. Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark. 3. ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. 4. Alma IT Systems, Barcelona, Spain. 5. Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland. 6. Department of Information and Communication Technologies, University Pompeu Fabra, Barcelona, Spain. 7. Department of Otorhinolaryngology, Technical University Munich, Munich, Germany. 8. Scanco Medical AG, Brüttisellen, Switzerland. 9. Department of ENT, Head and Neck Surgery, Inselspital, University of Bern, Bern, Switzerland. 10. Catalan Institution for Research and Advanced Studies (ICREA), Barcelona, Spain.
Abstract
PURPOSE: A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images. METHODS: From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a human cochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape. RESULTS: We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores. CONCLUSIONS: The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.
PURPOSE: A personalized estimation of the cochlear shape can be used to create computational anatomical models to aid cochlear implant (CI) surgery and CI audio processor programming ultimately resulting in improved hearing restoration. The purpose of this work is to develop and test a method for estimation of the detailed patient-specific cochlear shape from CT images. METHODS: From a collection of temporal bone [Formula: see text]CT images, we build a cochlear statistical deformation model (SDM), which is a description of how a humancochlea deforms to represent the observed anatomical variability. The model is used for regularization of a non-rigid image registration procedure between a patient CT scan and a [Formula: see text]CT image, allowing us to estimate the detailed patient-specific cochlear shape. RESULTS: We test the accuracy and precision of the predicted cochlear shape using both [Formula: see text]CT and CT images. The evaluation is based on classic generic metrics, where we achieve competitive accuracy with the state-of-the-art methods for the task. Additionally, we expand the evaluation with a few anatomically specific scores. CONCLUSIONS: The paper presents the process of building and using the SDM of the cochlea. Compared to current best practice, we demonstrate competitive performance and some useful properties of our method.
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Authors: Nerea Mangado; Mario Ceresa; Nicolas Duchateau; Hans Martin Kjer; Sergio Vera; Hector Dejea Velardo; Pavel Mistrik; Rasmus R Paulsen; Jens Fagertun; Jérôme Noailly; Gemma Piella; Miguel Ángel González Ballester Journal: Ann Biomed Eng Date: 2015-12-29 Impact factor: 3.934
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