Radeva, Petia,
Vascular and intravascular imaging trends, analysis, and challenges. Volume 2, Plaque characterization / Plaque characterization. Petia Radeva and Jasjit S. Suri. - 1 online resource (various pagings) : illustrations (some color). - [IOP release 6] IOP expanding physics, 2053-2563 . - IOP (Series). Release 6. IOP expanding physics. .
"Version: 20190801"--Title page verso.
Includes bibliographical references.
section I. Review on wall quantification, tissue characterization and coronary and carotid artery risk stratification. 1. Coronary and carotid artery calcium detection, its quantification and grayscale morphology-based risk stratification in mul 2. Risk of coronary artery disease : genetics and external factors -- 2.1. Introduction -- 2.2. External factors -- 2.3. Genetics of coronary artery disease -- 2.4. Multimodal coronary imaging -- 2.5. Association of CVD with other prevalent dise 3. Wall quantification and tissue characterization of the coronary artery -- 3.1. Introduction -- 3.2. Physics of image acquisition -- 3.3. Tissue characterization -- 3.4. A link between carotid and coronary artery disease -- 3.5. Wall quantific 4. Rheumatoid arthritis : its link to atherosclerosis imaging and cardiovascular risk assessment using machine-learning-based tissue characterization -- 4.1. Introduction -- 4.2. Search strategy -- 4.3. Brief description of the pathogensis of rh section II. Deep learning strategy for accurate lumen and carotid intima-media thickness measurement. 5. A deep-learning fully convolutional network for lumen characterization in diabetic patients using carotid ultrasound : a tool for stroke ris 6. Deep-learning strategy for accurate carotid intima-media thickness measurement : an ultrasound study on a Japanese diabetic cohort -- 6.1. Introduction -- 6.2. Data demographics and US acquisition -- 6.3. Methodology -- 6.4. Experimental prot section III. Association of morphological and echolucency-based phenotypes with HbA1c 7 Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. 7.1. Introduction -- 7.2. Patient demographics a 8. Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in a diabetes cohort -- 8.1. Introduction -- 8.2. Materials and methods -- 8.3. Results -- 8.3..4 Logistic regressio section IV. Deep learning strategy for accurate lumen and carotid intima-media thickness measurement. 9. Plaque tissue morphology-based stroke risk stratification using carotid ultrasound : a polling-based PCA learning paradigm -- 9.1. Introduct 10. Multiresolution-based coronary calcium volume measurement techniques from intravascular ultrasound videos -- 10.1. Introduction -- 10.2. Patient demographics and data acquisition -- 10.3. Methodology -- 10.4. Results -- 10.5. Performance eva 11. A cloud-based smart lumen diameter measurement tool for stroke risk assessment during multicenter clinical trials -- 11.1. Introduction -- 11.2. Materials and methods -- 11.3. Results -- 11.4. Discussion -- 11.5. Conclusion section V. Micro-electro-mechanical-system (MEMS) 12 A MEMS-based manufacturing technique of vascular bed. 12.1. Introduction -- 12.2. Microstructural anatomy of blood vessels -- 12.3. Modeling of blood vessels as a microsystem -- 12.4. Scaling
Cardiovascular Diseases (CVDs) are responsible for a third of all deaths in women and more than a half in men. Despite continuous improvements in treatment devices and imaging, there is still a rise in the morbidity rate from CVDs each year. Com
Academia and researchers, graduate students in medical imaging.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Professor Petia Radeva is a senior researcher and Full professor at the University of Barcelona. She is the head of Computer Vision and Machine Learning Consolidated Research Group (CVUB) at the University of Barcelona and the head of Medical Im
9780750320023 9780750320016
10.1088/2053-2563/ab0820 doi
Cardiovascular system--Diseases--Imaging.
Cardiovascular system--Diseases--Computer simulation.
Atherosclerotic plaque.
Cardiovascular Diseases--diagnostic imaging.
Cardiovascular Diseases.
Computer Simulation.
Plaque, Atherosclerotic.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
RC670 / .R348 2019eb vol. 2
616.1/075
WG 141 / R128v 2019eb vol. 2
Vascular and intravascular imaging trends, analysis, and challenges. Volume 2, Plaque characterization / Plaque characterization. Petia Radeva and Jasjit S. Suri. - 1 online resource (various pagings) : illustrations (some color). - [IOP release 6] IOP expanding physics, 2053-2563 . - IOP (Series). Release 6. IOP expanding physics. .
"Version: 20190801"--Title page verso.
Includes bibliographical references.
section I. Review on wall quantification, tissue characterization and coronary and carotid artery risk stratification. 1. Coronary and carotid artery calcium detection, its quantification and grayscale morphology-based risk stratification in mul 2. Risk of coronary artery disease : genetics and external factors -- 2.1. Introduction -- 2.2. External factors -- 2.3. Genetics of coronary artery disease -- 2.4. Multimodal coronary imaging -- 2.5. Association of CVD with other prevalent dise 3. Wall quantification and tissue characterization of the coronary artery -- 3.1. Introduction -- 3.2. Physics of image acquisition -- 3.3. Tissue characterization -- 3.4. A link between carotid and coronary artery disease -- 3.5. Wall quantific 4. Rheumatoid arthritis : its link to atherosclerosis imaging and cardiovascular risk assessment using machine-learning-based tissue characterization -- 4.1. Introduction -- 4.2. Search strategy -- 4.3. Brief description of the pathogensis of rh section II. Deep learning strategy for accurate lumen and carotid intima-media thickness measurement. 5. A deep-learning fully convolutional network for lumen characterization in diabetic patients using carotid ultrasound : a tool for stroke ris 6. Deep-learning strategy for accurate carotid intima-media thickness measurement : an ultrasound study on a Japanese diabetic cohort -- 6.1. Introduction -- 6.2. Data demographics and US acquisition -- 6.3. Methodology -- 6.4. Experimental prot section III. Association of morphological and echolucency-based phenotypes with HbA1c 7 Echolucency-based phenotype in carotid atherosclerosis disease for risk stratification of diabetes patients. 7.1. Introduction -- 7.2. Patient demographics a 8. Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in a diabetes cohort -- 8.1. Introduction -- 8.2. Materials and methods -- 8.3. Results -- 8.3..4 Logistic regressio section IV. Deep learning strategy for accurate lumen and carotid intima-media thickness measurement. 9. Plaque tissue morphology-based stroke risk stratification using carotid ultrasound : a polling-based PCA learning paradigm -- 9.1. Introduct 10. Multiresolution-based coronary calcium volume measurement techniques from intravascular ultrasound videos -- 10.1. Introduction -- 10.2. Patient demographics and data acquisition -- 10.3. Methodology -- 10.4. Results -- 10.5. Performance eva 11. A cloud-based smart lumen diameter measurement tool for stroke risk assessment during multicenter clinical trials -- 11.1. Introduction -- 11.2. Materials and methods -- 11.3. Results -- 11.4. Discussion -- 11.5. Conclusion section V. Micro-electro-mechanical-system (MEMS) 12 A MEMS-based manufacturing technique of vascular bed. 12.1. Introduction -- 12.2. Microstructural anatomy of blood vessels -- 12.3. Modeling of blood vessels as a microsystem -- 12.4. Scaling
Cardiovascular Diseases (CVDs) are responsible for a third of all deaths in women and more than a half in men. Despite continuous improvements in treatment devices and imaging, there is still a rise in the morbidity rate from CVDs each year. Com
Academia and researchers, graduate students in medical imaging.
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader, EPUB reader, or Kindle reader.
Professor Petia Radeva is a senior researcher and Full professor at the University of Barcelona. She is the head of Computer Vision and Machine Learning Consolidated Research Group (CVUB) at the University of Barcelona and the head of Medical Im
9780750320023 9780750320016
10.1088/2053-2563/ab0820 doi
Cardiovascular system--Diseases--Imaging.
Cardiovascular system--Diseases--Computer simulation.
Atherosclerotic plaque.
Cardiovascular Diseases--diagnostic imaging.
Cardiovascular Diseases.
Computer Simulation.
Plaque, Atherosclerotic.
Biomedical engineering.
TECHNOLOGY & ENGINEERING / Biomedical.
RC670 / .R348 2019eb vol. 2
616.1/075
WG 141 / R128v 2019eb vol. 2