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Toma, M, Lu, Y, Zhou, H, Garcia, J D. (1399). Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries. سامانه مدیریت نشریات علمی, 11(1), 115-122. doi: 10.31661/jbpe.v0i0.2001-1062
M Toma; Y Lu; H Zhou; J D Garcia. "Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries". سامانه مدیریت نشریات علمی, 11, 1, 1399, 115-122. doi: 10.31661/jbpe.v0i0.2001-1062
Toma, M, Lu, Y, Zhou, H, Garcia, J D. (1399). 'Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries', سامانه مدیریت نشریات علمی, 11(1), pp. 115-122. doi: 10.31661/jbpe.v0i0.2001-1062
Toma, M, Lu, Y, Zhou, H, Garcia, J D. Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries. سامانه مدیریت نشریات علمی, 1399; 11(1): 115-122. doi: 10.31661/jbpe.v0i0.2001-1062

Thresholding Segmentation Errors and Uncertainty with Patient-Specific Geometries

Journal of Biomedical Physics and Engineering
مقاله 14، دوره 11، شماره 1، فروردین و اردیبهشت 2021، صفحه 115-122    اصل مقاله (1.23 M)
نوع مقاله: Technical Note
شناسه دیجیتال (DOI): 10.31661/jbpe.v0i0.2001-1062
نویسندگان
M Toma* 1، 2؛ Y Lu3؛ H Zhou3؛ J D Garcia4
1PhD, Department of Osteopathic Manipulative Medicine, College of Osteopathic Medicine, New York Institute of Technology, Old Westbury Campus, Northern Boulevard, Old Westbury, New York 11568-8000, USA
2PhD, Department of Mechanical Engineering, College of Engineering & Computing Sciences, New York Institute of Technology, Old Westbury Campus, Northern Boulevard, Old Westbury, New York 11568-8000, USA
3MSc, Department of Mechanical Engineering, College of Engineering & Computing Sciences, New York Institute of Technology, Old Westbury Campus, Northern Boulevard, Old Westbury, New York 11568-8000, USA
4DPT, Byrdine F. Lewis College of Nursing and Health Professions, Georgia State University, Urban Life Building, Suite 1280, Atlanta, GA, USA
چکیده
Computer simulations provide virtual hands-on experience when actual hands-on experience is not possible. To use these simulations in medical science, they need to be able to predict the behavior of actual processes with actual patient-specific geometries. Many uncertainties enter in the process of developing these simulations, starting with creating the geometry. The actual patient-specific geometry is often complex and hard to process. Usually, simplifications to the geometry are introduced in exchange for faster results. However, when simplified, these simulations can no longer be considered patient-specific as they do not represent the actual patient they come from. The ultimate goal is to keep the geometries truly patient-specific without any simplification. However, even without simplifications, the patient-specific geometries are based on medical imaging modalities and consequent use of numerical algorithms to create and process the 3D surface. Multiple users are asked to process medical images of a complex geometry. Their resulting geometries are used to assess how the user’s choices determine the resulting dimensions of the 3D model. It is shown that the resulting geometry heavily depends on user’s choices.
کلیدواژه‌ها
Image Processing, Computer-Assisted, Errors؛ Uncertainty؛ Thresholding؛ Patient-Specific Modeling
مراجع
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  7. Toma M, Bloodworth CH, Pierce EL, Einstein DR, Cochran RP, Yoganathan AP, Kunzelman KS. Fluid-structure interaction analysis of ruptured mitral chordae tendineae. Annals of Biomedical Engineering. 2017;45(3):619-31. doi: 10.1007/s10439-016-1727-y.
  8. Bloodworth CH, Pierce EL, Easley TF, Drach A, Khalighi AH, Toma M, Jensen MO, Sacks MS, Yoganathan AP. Ex vivo methods for informing computational models of the mitral valve. Annals of Biomedical Engineering. 2017;45(2):496-507. doi: 10.1007/s10439-016-1734-z. PubMed PMID: 27699507. PubMed PMCID: PMC5300906.
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  10. Wei ZA, Sonntag SJ, Toma M, Singh-Gryzbon S, Sun W. Computational fluid dynamics assessment associated with transcatheter heart valve prostheses: a position paper of the ISO Working Group. Cardiovasc Eng Technol. 2018;9(3):289-99. doi: 10.1007/s13239-018-0349-y. PubMed PMID: 29675697. PubMed PMCID: PMC6095731.
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