Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography

Imaging techniques are a key tool in the diagnosis of disease. X-rays, ultrasound, CAT, and PET scans are now routinely used as a preliminary step to determine the extent of a disease and the need for and type of treatment (Tearney et al. 2006). These techniques generate vast quantities of data. The...
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Autor*in:	Ronny Shalev [verfasserIn] Daisuke Nakamura Setsu Nishino Andrew M Rollins Hiram G Bezerra David L Wilson Soumya Ray

Format:	Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Coronary vessels Classification Blood clots Heart attacks Researchers Medical imaging Automation Visualization Stents Cardiovascular disease

Übergeordnetes Werk:	Enthalten in: AI magazine - Menlo Park, Calif. : AAAI, 1980, 38(2017), 1, Seite 61
Übergeordnetes Werk:	volume:38 ; year:2017 ; number:1 ; pages:61

Links:	Link aufrufen

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title_sort	automated volumetric intravascular plaque classification using optical coherence tomography
title_auth	Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography
abstract	Imaging techniques are a key tool in the diagnosis of disease. X-rays, ultrasound, CAT, and PET scans are now routinely used as a preliminary step to determine the extent of a disease and the need for and type of treatment (Tearney et al. 2006). These techniques generate vast quantities of data. The images that are produced must typically be analyzed by trained clinicians. This is extremely labor intensive, expensive, and can be prone to error. Thus, there is a need for, and an opportunity to, improve the quality of healthcare systems by developing automated aids to assist in this process. Given the patient-critical outcomes of the image-analysis process, a human analyst must always remain in the loop. In this article, the authors have discussed an important emerging application: an automated approach for early plaque detection in blood vessels. Their approach analyzes IVOCT images to solve this task.
abstractGer	Imaging techniques are a key tool in the diagnosis of disease. X-rays, ultrasound, CAT, and PET scans are now routinely used as a preliminary step to determine the extent of a disease and the need for and type of treatment (Tearney et al. 2006). These techniques generate vast quantities of data. The images that are produced must typically be analyzed by trained clinicians. This is extremely labor intensive, expensive, and can be prone to error. Thus, there is a need for, and an opportunity to, improve the quality of healthcare systems by developing automated aids to assist in this process. Given the patient-critical outcomes of the image-analysis process, a human analyst must always remain in the loop. In this article, the authors have discussed an important emerging application: an automated approach for early plaque detection in blood vessels. Their approach analyzes IVOCT images to solve this task.
abstract_unstemmed	Imaging techniques are a key tool in the diagnosis of disease. X-rays, ultrasound, CAT, and PET scans are now routinely used as a preliminary step to determine the extent of a disease and the need for and type of treatment (Tearney et al. 2006). These techniques generate vast quantities of data. The images that are produced must typically be analyzed by trained clinicians. This is extremely labor intensive, expensive, and can be prone to error. Thus, there is a need for, and an opportunity to, improve the quality of healthcare systems by developing automated aids to assist in this process. Given the patient-critical outcomes of the image-analysis process, a human analyst must always remain in the loop. In this article, the authors have discussed an important emerging application: an automated approach for early plaque detection in blood vessels. Their approach analyzes IVOCT images to solve this task.
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title_short	Automated Volumetric Intravascular Plaque Classification Using Optical Coherence Tomography
url	http://search.proquest.com/docview/1888749268
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author2	Daisuke Nakamura Setsu Nishino Andrew M Rollins Hiram G Bezerra David L Wilson Soumya Ray
author2Str	Daisuke Nakamura Setsu Nishino Andrew M Rollins Hiram G Bezerra David L Wilson Soumya Ray
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up_date	2024-07-03T13:32:16.396Z
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