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Improving Gross Anatomy: Enhancing t...

Lewis, Steven A.

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Improving Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans and Models.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans and Models./
Author:	Lewis, Steven A.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	174 p.
Notes:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
Subject:	Biomedical engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30317780
ISBN:	9798379735074

Improving Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans and Models.
Lewis, Steven A.

Improving Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans and Models. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 174 p.

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--State University of New York at Buffalo, 2023.

Gross anatomy has been primarily taught and researched through the use of cadaveric dis-section for hundreds of years. COVID-19 demonstrated that medical schools cannot sustain gross anatomy education and research through these traditional methods alone. There is a desperate need for the development of new gross anatomy courses that leverage these traditional approaches, but also modern computer vision and biomedical engineering techniques. These techniques are becoming more and more important to the fields of medicine. The purpose of this thesis is to explore the ways in which computer vision, modeling, and analysis can enhance traditional gross anatomy education and research. This is achieved through the analysis of cadaveric, non-contrast enhanced (NCE), whole-body CT imaging, and 3D modelling of anatomical structures.In this work, we hypothesize that state-of-the art computer vision can enhance the information content of these CT images and 3D models for the purpose of gross anatomy education and research. We explore this hypothesis in a variety of ways. We first analyze different physical and computational methods to enhance the image quality of cadaveric, NCE, whole-body CT. Additionally, we demonstrate that cadaveric, NCE whole-body CT can be leveraged for multi-organ segmentation. We also use cadaveric segmentations and 3D models to supplement our understanding of the rare disease Seckel Syndrome, and the variation present in kidney models. Finally, we apply this framework to the teaching of graduate students in gross anatomy. Our results indicate that a pipeline such as this can in fact improve gross anatomy education and research.

ISBN: 9798379735074Subjects--Topical Terms:

535387
Biomedical engineering.
Subjects--Index Terms:

Artificial intelligence

Improving Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans and Models.
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245 1 0 $a Improving Gross Anatomy: Enhancing the Information Content of Cadaveric CT Scans and Models.
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300 $a 174 p.
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Doyle, Scott.
502 $a Thesis (Ph.D.)--State University of New York at Buffalo, 2023.
520 $a Gross anatomy has been primarily taught and researched through the use of cadaveric dis-section for hundreds of years. COVID-19 demonstrated that medical schools cannot sustain gross anatomy education and research through these traditional methods alone. There is a desperate need for the development of new gross anatomy courses that leverage these traditional approaches, but also modern computer vision and biomedical engineering techniques. These techniques are becoming more and more important to the fields of medicine. The purpose of this thesis is to explore the ways in which computer vision, modeling, and analysis can enhance traditional gross anatomy education and research. This is achieved through the analysis of cadaveric, non-contrast enhanced (NCE), whole-body CT imaging, and 3D modelling of anatomical structures.In this work, we hypothesize that state-of-the art computer vision can enhance the information content of these CT images and 3D models for the purpose of gross anatomy education and research. We explore this hypothesis in a variety of ways. We first analyze different physical and computational methods to enhance the image quality of cadaveric, NCE, whole-body CT. Additionally, we demonstrate that cadaveric, NCE whole-body CT can be leveraged for multi-organ segmentation. We also use cadaveric segmentations and 3D models to supplement our understanding of the rare disease Seckel Syndrome, and the variation present in kidney models. Finally, we apply this framework to the teaching of graduate students in gross anatomy. Our results indicate that a pipeline such as this can in fact improve gross anatomy education and research.
590 $a School code: 0656.
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650 4 $a Cellular biology. $3 3172791
650 4 $a Pathology. $3 643180
653 $a Artificial intelligence
653 $a Cadaveric CT
653 $a Generative modelling
653 $a Machine learning
653 $a Medical physics
653 $a Radiology
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690 $a 0379
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792 $a 2023
793 $a English
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