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An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology

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An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology. / Waldmann, Moritz; Grosch, Alice; Witzler, Christian; Lehner, Matthias; Benda, Odo; Koch, Walter; Vogt, Klaus; Kohn, Christopher; Schröder, Wolfgang; Göbbert, Jens Henrik; Lintermann, Andreas.

In: MED BIOL ENG COMPUT, Vol. 60, No. 2, 02.2022, p. 365-391.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Waldmann, M, Grosch, A, Witzler, C, Lehner, M, Benda, O, Koch, W, Vogt, K, Kohn, C, Schröder, W, Göbbert, JH & Lintermann, A 2022, 'An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology', MED BIOL ENG COMPUT, vol. 60, no. 2, pp. 365-391. https://doi.org/10.1007/s11517-021-02446-3

APA

Waldmann, M., Grosch, A., Witzler, C., Lehner, M., Benda, O., Koch, W., Vogt, K., Kohn, C., Schröder, W., Göbbert, J. H., & Lintermann, A. (2022). An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology. MED BIOL ENG COMPUT, 60(2), 365-391. https://doi.org/10.1007/s11517-021-02446-3

Vancouver

Waldmann M, Grosch A, Witzler C, Lehner M, Benda O, Koch W et al. An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology. MED BIOL ENG COMPUT. 2022 Feb;60(2):365-391. https://doi.org/10.1007/s11517-021-02446-3

Bibtex

@article{df4ac67efa264acd9157619f9fad570f,
title = "An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology",
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. Graphical Abstract Workflow for enhanced diagnostics in rhinology.",
keywords = "Algorithms, Computer Simulation, Machine Learning, Software, Workflow",
author = "Moritz Waldmann and Alice Grosch and Christian Witzler and Matthias Lehner and Odo Benda and Walter Koch and Klaus Vogt and Christopher Kohn and Wolfgang Schr{\"o}der and G{\"o}bbert, {Jens Henrik} and Andreas Lintermann",
note = "{\textcopyright} 2021. The Author(s).",
year = "2022",
month = feb,
doi = "10.1007/s11517-021-02446-3",
language = "English",
volume = "60",
pages = "365--391",
journal = "MED BIOL ENG COMPUT",
issn = "0140-0118",
publisher = "Springer",
number = "2",

}

RIS

TY - JOUR

T1 - An effective simulation- and measurement-based workflow for enhanced diagnostics in rhinology

AU - Waldmann, Moritz

AU - Grosch, Alice

AU - Witzler, Christian

AU - Lehner, Matthias

AU - Benda, Odo

AU - Koch, Walter

AU - Vogt, Klaus

AU - Kohn, Christopher

AU - Schröder, Wolfgang

AU - Göbbert, Jens Henrik

AU - Lintermann, Andreas

N1 - © 2021. The Author(s).

PY - 2022/2

Y1 - 2022/2

N2 - 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. Graphical Abstract Workflow for enhanced diagnostics in rhinology.

AB - 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. Graphical Abstract Workflow for enhanced diagnostics in rhinology.

KW - Algorithms

KW - Computer Simulation

KW - Machine Learning

KW - Software

KW - Workflow

U2 - 10.1007/s11517-021-02446-3

DO - 10.1007/s11517-021-02446-3

M3 - SCORING: Journal article

C2 - 34950998

VL - 60

SP - 365

EP - 391

JO - MED BIOL ENG COMPUT

JF - MED BIOL ENG COMPUT

SN - 0140-0118

IS - 2

ER -