2021
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Inthavong, Kiao; Wong, Eugene; Tu, Jiyuan; Singh, Narinder (Ed.) Clinical and Biomedical Engineering in the Human Nose Book Springer, 2021, ISBN: 978-981-15-6716-2. @book{inthavong2021clinicalb,
title = {Clinical and Biomedical Engineering in the Human Nose},
editor = {Inthavong, Kiao and Wong, Eugene and Tu, Jiyuan and Singh, Narinder},
url = {https://link.springer.com/book/10.1007/978-981-15-6716-2#toc},
isbn = {978-981-15-6716-2},
year = {2021},
date = {2021-01-01},
publisher = {Springer},
abstract = {This book explores computational fluid dynamics in the context of the human nose, allowing readers to gain a better understanding of its anatomy and physiology and integrates recent advances in clinical rhinology, otolaryngology and respiratory physiology research. It focuses on advanced research topics, such as virtual surgery, AI-assisted clinical applications and therapy, as well as the latest computational modeling techniques, controversies, challenges and future directions in simulation using CFD software. Presenting perspectives and insights from computational experts and clinical specialists (ENT) combined with technical details of the computational modeling techniques from engineers, this unique reference book will give direction to and inspire future research in this emerging field.},
keywords = {Computational Fluid Dynamics, Convolutional Neural Networks, Nasal cavity flows, Respiratory Flow Computation},
pubstate = {published},
tppubtype = {book}
}
This book explores computational fluid dynamics in the context of the human nose, allowing readers to gain a better understanding of its anatomy and physiology and integrates recent advances in clinical rhinology, otolaryngology and respiratory physiology research. It focuses on advanced research topics, such as virtual surgery, AI-assisted clinical applications and therapy, as well as the latest computational modeling techniques, controversies, challenges and future directions in simulation using CFD software. Presenting perspectives and insights from computational experts and clinical specialists (ENT) combined with technical details of the computational modeling techniques from engineers, this unique reference book will give direction to and inspire future research in this emerging field. | |
2020
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Lehner, Matthias; Benda, Odo Machine Learning based Image Segmentation with Convolutional Neural Networks Miscellaneous EMBEC Abstract Book Contribution, 2020, ISBN: 978-961-243-411-3. @misc{Leh20,
title = {Machine Learning based Image Segmentation with Convolutional Neural Networks},
author = {Lehner, Matthias and Benda, Odo },
editor = {Tomaž Jarm, Samo Mahnič-Kalamiza, Aleksandra Cvetkoska, Damijan Miklavčič},
url = {https://www.embec2020.org/wp-content/uploads/2020/11/EMBEC2020_Book_of_Abstracts.pdf},
isbn = {978-961-243-411-3},
year = {2020},
date = {2020-11-30},
abstract = {In this work, results are presented for a CNN architecture which was developed specifically to classify different segments in CT images of the nasal cavity and paranasal sinuses automatically and accurately. This approach makes it possible to generate high quality segmentations of the frontal, maxillary, and sphenoid sinuses on both sides of the body, the oral and nasal cavities, bone, tissues, as well as the air outside the head surrounding the patient’s body in order to remove the latter conveniently.},
howpublished = {EMBEC Abstract Book Contribution},
keywords = {3D Model Generation, Convolutional Neural Networks, CT Images, nasal cavity},
pubstate = {published},
tppubtype = {misc}
}
In this work, results are presented for a CNN architecture which was developed specifically to classify different segments in CT images of the nasal cavity and paranasal sinuses automatically and accurately. This approach makes it possible to generate high quality segmentations of the frontal, maxillary, and sphenoid sinuses on both sides of the body, the oral and nasal cavities, bone, tissues, as well as the air outside the head surrounding the patient’s body in order to remove the latter conveniently. | |
Feng, Yu; Hayati, Hamideh; Bates, Alister J.; Walter, Koch; Matthias, Lehner; Odo, Benda; Ramiro, Ortiz; Gerda, Koch Clinical CFD Applications 2 Book Chapter In: Ithavong, Kiao; Singh, Narinder; Wong, Eurgene; Tu, Jiyuang (Ed.): Clinical and Biomedical Engineering in the Human Nose - A Computational Fluid Dynamics Approach, vol. 1, Chapter 10, pp. 225-253, Springer Nature Singapore Pte Ltd. 2021, 1, 2020, ISBN: 978-981-15-6715-5. @inbook{Koch2020,
title = {Clinical CFD Applications 2},
author = {Yu Feng and Hamideh Hayati and Alister J. Bates and Koch Walter and Lehner Matthias and Benda Odo and Ortiz Ramiro and Koch Gerda },
editor = {Ithavong, Kiao and Singh, Narinder and Wong, Eurgene and Tu, Jiyuang},
url = {https://link.springer.com/chapter/10.1007/978-981-15-6716-2_10},
doi = {10.1007/978-981-15-6716-2_10},
isbn = {978-981-15-6715-5},
year = {2020},
date = {2020-10-17},
booktitle = {Clinical and Biomedical Engineering in the Human Nose - A Computational Fluid Dynamics Approach},
volume = {1},
pages = {225-253},
publisher = {Springer Nature Singapore Pte Ltd. 2021},
edition = {1},
chapter = {10},
abstract = {This chapter is the second of the two chapters demonstrating the wide variety of CFD studies in clinical applications presented from leading researchers in their respective fields. This chapter covers the latest research techniques and outcomes in whole lung modelling; Modeling the Effect of Airway Motion Using Dynamic Imaging; and Automatic reconstruction of the nasal geometry from CT scans.},
keywords = {Artificial Intelligence, Automated Segmentation, CFD Applications, Convolutional Neural Networks, Mesh Generation, Nasal cavity flows},
pubstate = {published},
tppubtype = {inbook}
}
This chapter is the second of the two chapters demonstrating the wide variety of CFD studies in clinical applications presented from leading researchers in their respective fields. This chapter covers the latest research techniques and outcomes in whole lung modelling; Modeling the Effect of Airway Motion Using Dynamic Imaging; and Automatic reconstruction of the nasal geometry from CT scans. | |