Computer-Assisted Diagnosis: Diabetes and Cardiovascular Disease brings together multifaceted information on research and clinical applications from an academic, clinical, bioengineering and bioinformatics perspective. The editors provide a stellar, diverse list of authors to explore this interesting field. Academic researchers, bioengineers, new investigators and students interested in diabetes and heart disease need an authoritative reference to reduce the amount of time spent on source-searching so they can spend more time on actual research and clinical application. This reference accomplishes this with contributions by authors from around the world.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xiii 8. Biography of the editors - p. xv 9. Acknowledgments - p. xv 10. 1. Cardiac imaging: Clinical principles and applications - p. 1 11. 2. Left ventricle segmentation for cine MR using deep learning - p. 37 12. 3. Computational methods for identifying left ventricle heart pathologies - p. 67 13. 4. Diabetes mellitus and atrial fibrillation—Untying the Gordian Knot - p. 95 14. 5. Carotenoids in diabetes, retinopathy, and cardiovascular risk - p. 123 15. 6. Nanomedicine approaches for the diagnosis, treatment, and theragnosis of diabetes mellitus, hypertension, and their associated cardiovascular diseases - p. 153 16. 7. Data-driven feature learning for myocardial registration and segmentation - p. 185 17. 8. Diabetes and coronary circulation: From pathology to imaging - p. 227 18. 9. Prediction of paravalvular leak post transcatheter aortic valve replacement - p. 269 19. 10. Clinical imaging techniques for assessing vascular risk and complications in the lower extremities - p. 291 20. 11. Management of heart failure in the context of type 2 diabetes - p. 319 21. Index - p. 341
Computer-Assisted Diagnosis: Diabetes and Cardiovascular Disease brings together multifaceted information on research and clinical applications from an academic, clinical, bioengineering and bioinformatics perspective. The editors provide a stellar, diverse list of authors to explore this interesting field. Academic researchers, bioengineers, new investigators and students interested in diabetes and heart disease need an authoritative reference to reduce the amount of time spent on source-searching so they can spend more time on actual research and clinical application. This reference accomplishes this with contributions by authors from around the world.
1. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iii 6. Dedication - p. iii 7. Contributors - p. xiii 8. Biography of the editors - p. xv 9. Acknowledgments - p. xv 10. 1. Cardiac imaging: Clinical principles and applications - p. 1 11. 2. Left ventricle segmentation for cine MR using deep learning - p. 37 12. 3. Computational methods for identifying left ventricle heart pathologies - p. 67 13. 4. Diabetes mellitus and atrial fibrillation—Untying the Gordian Knot - p. 95 14. 5. Carotenoids in diabetes, retinopathy, and cardiovascular risk - p. 123 15. 6. Nanomedicine approaches for the diagnosis, treatment, and theragnosis of diabetes mellitus, hypertension, and their associated cardiovascular diseases - p. 153 16. 7. Data-driven feature learning for myocardial registration and segmentation - p. 185 17. 8. Diabetes and coronary circulation: From pathology to imaging - p. 227 18. 9. Prediction of paravalvular leak post transcatheter aortic valve replacement - p. 269 19. 10. Clinical imaging techniques for assessing vascular risk and complications in the lower extremities - p. 291 20. 11. Management of heart failure in the context of type 2 diabetes - p. 319 21. Index - p. 341
