| Waldmann, Moritz; Grosch, Alice; Witzler, Christian; Lehner, Matthias; Benda, Odo; Koch, Walter; Vogt, Klaus; Kohn, Christopher; Schröder, Wolfgang; Göbbert, Jens Henrik; Lintermann, Andreas An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology Artikel In: Medical & Biological Engineering & Computing , 2021. @article{Waldmann2021,
title = {An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology},
author = {Waldmann, Moritz and Grosch, Alice and Witzler, Christian and Lehner, Matthias and Benda, Odo and Koch, Walter and
Vogt, Klaus and Kohn, Christopher and Schröder, Wolfgang and Göbbert, Jens Henrik and Lintermann, Andreas },
editor = {Springer },
url = {https://link.springer.com/content/pdf/10.1007/s11517-021-02446-3.pdf},
doi = {10.1007/s11517-021-02446-3},
year = {2021},
date = {2021-12-23},
urldate = {2021-12-23},
journal = {Medical & Biological Engineering & Computing },
abstract = {Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a Jupyter environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation.},
keywords = {Computational Fluid Dynamics, High performance computing, Machine Learning, Rhinology},
pubstate = {published},
tppubtype = {article}
}
Physics-based analyses have the potential to consolidate and substantiate medical diagnoses in rhinology. Such methods are frequently subject to intense investigations in research. However, they are not used in clinical applications, yet. One issue preventing their direct integration is that these methods are commonly developed as isolated solutions which do not consider the whole chain of data processing from initial medical to higher valued data. This manuscript presents a workflow that incorporates the whole data processing pipeline based on a Jupyter environment. Therefore, medical image data are fully automatically pre-processed by machine learning algorithms. The resulting geometries employed for the simulations on high-performance computing systems reach an accuracy of up to 99.5% compared to manually segmented geometries. Additionally, the user is enabled to upload and visualize 4-phase rhinomanometry data. Subsequent analysis and visualization of the simulation outcome extend the results of standardized diagnostic methods by a physically sound interpretation. Along with a detailed presentation of the methodologies, the capabilities of the workflow are demonstrated by evaluating an exemplary medical case. The pipeline output is compared to 4-phase rhinomanometry data. The comparison underlines the functionality of the pipeline. However, it also illustrates the influence of mucosa swelling on the simulation. |
| Lintermann, Andreas; Göbbert, Jens Henrik; Vogt, Klaus; Koch, Walter; Hetzel, Alexander Rhinodiagnost - Morphological and functional precision diagnostics of nasal cavities Artikel In: InSiDE, Innovatives Supercomputing in Deutschland, Bd. 15, Nr. 2, S. 106-109, 2017. @article{RhinoAll,
title = {Rhinodiagnost - Morphological and functional precision diagnostics of nasal cavities},
author = {Lintermann, Andreas and Göbbert, Jens Henrik and Vogt, Klaus and Koch, Walter and Hetzel, Alexander},
editor = {Gauss Center for Supercomputing (GCS), High-Perfomance Computing Center Stuttart (HLRS)},
url = {http://rhinodiagnost.eu/wp-content/uploads/2017/11/InSiDE-Innovatives-Supercomputing-in-Deutschland-2017-Rhinodiagnost-Morphological-and-functional-precision-diagnostics-of-nasal-c.pdf, Rhinodiagnost - Morphological and functional precision diagnostics of nasal cavities},
year = {2017},
date = {2017-08-31},
journal = {InSiDE, Innovatives Supercomputing in Deutschland},
volume = {15},
number = {2},
pages = {106-109},
keywords = {Computational Fluid Dynamics, Diagnostics, In-situ computational steering, Nasal respiration, Rhinology, Rhinomanometry},
pubstate = {published},
tppubtype = {article}
}
|
