Desktop version

Home arrow Health

References

  • 1. Thompson DAW (1917) On Shape and Growth. Cambridge University Press, Cambridge
  • 2. Fukunaga K (1972) Introduction to statistical pattern recognition. Academic press, New York
  • 3. Szeliski R (2010) Computer vision: algorithms and applications. Springer, New York
  • 4. Russell S, Norvig P (2009) Artificial intelligence: a modern approach, 3rd edn. Prentice-Hall
  • 5. Hohne KH et al (1995) A new representation of knowledge concerning human anatomy and function. Nat Med 1(6):506—511
  • 6. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321-331
  • 7. Cootes TF, Edwards GJ, Taylor GJ (1999) Comparing active shape models with active appearance models. In BMVC
  • 8. Pennec X (2009) Statistical computing on manifolds: from Riemannian geometry to computational anatomy. In: Emerging trends in visual computing. Springer, Berlin, pp 347-386
  • 9. Suzuki K (2012) Machine learning in computer-aided diagnosis: medical imaging intelligence. Medical Information Science Reference, Hershey
  • 10. SPM. Available from: http://www.fil.ion.ucl.ac.uk/spm/software/
  • 11. Computational anatomy for computer-aided diagnosis and therapy: frontiers of medical image sciences. Available from: http://www.comp-anatomy.org/wiki/ index.php?Computational%20Anatomy
  • 12. The Human Connectome Project. Available from: http://www.humanconnectomeproject.org/
  • 13. Physiome Project. Available from: http://physiomeproject.org
  • 14. Winkelmann A (2007) Anatomical dissection as a teaching method in medical school: a review of the evidence. Med Educ 41(1):15-22
  • 15. Turney B (2007) Anatomy in a modern medical curriculum. Ann R Coll Surg Engl 89(2):104
  • 16. Purkayastha S, Paraskevas P, Darzi A (2007) Anatomy crisis: make surgeons more active in teaching anatomy at all levels. Br Med J 334(7585):110
  • 17. Tanaka M et al (2013) IT system and network required for operation of Ai. INNERVISION 28(1):69-71
  • 18. Noriki S et al (2012) Positioning of Ai as a tool of the lifelong education which used IT as the base. INNERVISION 27(1):30-33
  • 19. Ai center of University of Fukui. Available from: http://ai.labos.ac/
  • 20. Iino S (2014) Present condition of anatomy practice and image education in the university of Fukui. INNERVISION 29(1):46-48
  • 21. Porter R (1999) The greatest benefit to mankind: a medical history of humanity (The Norton history of science). WW Norton & Company, New York
  • 22. Duckworth W (1962) Galen: on anatomical procedures: the later books. Cambridge University Press, Cambridge
  • 23. Mason SF (1962) A history of the sciences. Collier Books, New York
  • 24. Thali MJ et al (2003) Virtopsy, a new imaging horizon in forensic pathology: virtual autopsy by postmortem multislice computed tomography (MSCT) and magnetic resonance imaging (MRI)-a feasibility study. J Forensic Sci 48(2):386-403
  • 25. Korf H-W et al (2008) The dissection course-necessary and indispensable for teaching anatomy to medical students. Ann Anat Anat Anz 190(1):16-22
  • 26. McLachlan JC (2004) New path for teaching anatomy: living anatomy and medical imaging vs. dissection. Anat Rec B New Anat 281(1):4-5
  • 27. Slavin K (1997) The visible human project. Surg Neurol 48(6):638
  • 28. Ackerman M et al (1994) The visible human data set: an image resource for anatomical visualization. Medinfo MEDINFO 8:1195-1198
  • 29. Ackerman MJ (1991) The visible human project. J Biocommun 18(2): 14
  • 30. Nobuoka D et al (2014) Surgical education using a multi-viewpoint and multi-layer threedimensional atlas of surgical anatomy (with video). J Hepatobiliary Pancreat Sci 21(8):556- 561
  • 31. Matsuo T, Takeda Y, Ohtsuka A (2013) Stereoscopic three-dimensional images of an anatomical dissection of the eyeball and orbit for educational purposes. Acta Med Okayama 67:87-91
  • 32. Tainaka K et al (2014) Whole-body imaging with single-cell resolution by tissue decoloriza- tion. Cell 159(4):911-924
  • 33. Moniz E, de Carvalho L, Lima A (1931) Angiopneumographie. Presse Med 39:996-999
  • 34. Ichikawa H (1967) X-Ray diagnosis of early gastric cancer. J Gastroenterol 2(4):277-281
  • 35. Takahashi S (1949) A new device to get a radiological section figure of body. Tohoku J Exp Med 51(1-2):70-70
  • 36. Hounsfield GN (1973) Computerized transverse axial scanning (tomography): Part 1. Description of system. Br J Radiol 46(552):1016-1022
  • 37. Todo G et al (1982) [High resolution CT (HR-CT) for the evaluation of pulmonary peripheral disorders]. Rinsho hoshasen. Clin Radiogr 27(12):1319-1326
  • 38. Kalender W et al (1990) Spiral CT: an innovative method of volumetric recording. Pt. 1. Roentgenpraxis 43 : 323 - 330
  • 39. Hohne K, Riemer M, Tiede U (1988) Volume rendering of 3D-tomographic imagery. In: Information processing in medical imaging. Springer, New York
  • 40. Sosna J et al (2003) CT colonography of colorectal polyps: a metaanalysis. Am J Roentgenol 181(6): 1593—1598
  • 41. Yoshida H, Nappi J (2001) Three-dimensional computer-aided diagnosis scheme for detection of colonic polyps. IEEE Trans Med Imaging 20(12):1261-1274
  • 42. Vinning D, Shitrin R, Haponik E (1994) Virtual bronchoscopy. Radiology 193(P):261
  • 43. Meyers PH et al (1964) Automated computer analysis of radiographic images 1. Radiology 83(6):1029-1034
  • 44. Winsberg F et al (1967) Detection of radiographic abnormalities in mammograms by means of optical scanning and computer analysis 1. Radiology 89(2):211-215
  • 45. The QIBA website. Available from: https://www.rsna.org/QIBA.aspx
  • 46. Shimizu A et al (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surg 2(3-4):135-142
  • 47. Kobatake H (2007) Future cad in multi-dimensional medical images:-project on multi-organ, multi-disease cad system. Comput Med Imaging Graph 31(4):258-266
  • 48. Shimizu A, Sato Y (2006) Construction of statistical atlas of abdominal organs and its application to multi-organ segmentation. Med Imaging Technol 24(3):153-160
  • 49. The SPM website. Available from: http://www.fil.ion.ucl.ac.uk/spm/
  • 50. Kobatake H (2011) Aims of the research project “computational anatomy”. Med Imaging Technol 29(3):99-103
  • 51. MacKee GM (1921) X-rays and radium in the treatment of diseases of the skin. Lea & Febiger, Philadelphia
  • 52. Takahashi S (1965) Conformation radiotherapy. Rotation techniques as applied to radiography and radiotherapy of cancer. Acta Radiol Diagn Suppl 242: 1C— 1 +
  • 53. Webb S (2001) Intensity-modulated radiation therapy. CRC Press, Chapman Hall
  • 54. Shirato H et al (2000) Four-dimensional treatment planning and fluoroscopic real-time tumor tracking radiotherapy for moving tumor. Int J Rad Oncol Biol Phys 48(2):435-442
  • 55. Dawson LA, Sharpe MB (2006) Image-guided radiotherapy: rationale, benefits, and limitations. Lancet Oncol 7(10):848-858
  • 56. Evans PM (2008) Anatomical imaging for radiotherapy. Phys Med Biol 53(12):R151
  • 57. Ministry of Internal Affairs and Communications (MIC). 2011; Available from: http:// www.stat.go.jp/data/jinsui/pdf/201102.pdf
  • 58. Wambersie A, Landber T (1999) ICRU report 62: prescribing, recording and reporting photon beam therapy. Supplement to ICRU report, 50
  • 59. Onishi H et al (2011) Stereotactic body radiotherapy (SBRT) for operable stage I non-smallcell lung cancer: can SBRT be comparable to surgery? Int J Rad Oncol Biol Phys 81(5): 13521358
  • 60. Van de Steene J et al (2002) Definition of gross tumor volume in lung cancer: inter-observer variability. Radiother Oncol 62(1):37-49
  • 61. El Naqa I et al (2007) Concurrent multimodality image segmentation by active contours for radiotherapy treatment planninga. Med Phys 34(12):4738-4749
  • 62. Nakamura K et al (2008) Variation of clinical target volume definition among Japanese radiation oncologists in external beam radiotherapy for prostate cancer. Jpn J Clin Oncol 38(4):275-280
  • 63. Geets X et al (2007) A gradient-based method for segmenting FDG-PET images: methodology and validation. Eur J Nucl Med Mol Imaging 34(9):1427-1438
  • 64. Day E et al (2009) A region growing method for tumor volume segmentation on PET images for rectal and anal cancer patients. Med Phys 36(10):4349-4358
  • 65. Hatt M et al (2011) PET functional volume delineation: a robustness and repeatability study. Eur J Nucl Med Mol Imaging 38(4):663-672
  • 66. Ploquin N, Rangel A, Dunscombe P (2008) Phantom evaluation of a commercially available three modality image guided radiation therapy system. Med Phys 35(12):5303-5311
  • 67. Wang Z et al (2009) Refinement of treatment setup and target localization accuracy using three-dimensional cone-beam computed tomography for stereotactic body radiotherapy. Int J Rad Oncol Biol Phys 73(2):571-577
  • 68. Hong J, Hashizume M (2010) An effective point-based registration tool for surgical navigation. Surg Endosc 24(4):944-948
  • 69. Souzaki R et al (2013) An augmented reality navigation system for pediatric oncologic surgery based on preoperative CT and MRI images. J Pediatr Surg 48(12):2479-2483
  • 70. Maeda T et al (2009) Tumor ablation therapy of liver cancers with an open magnetic resonance imaging-based navigation system. Surg Endosc 23(5):1048-1053
  • 71. Tomikawa M et al (2010) Real-time 3-dimensional virtual reality navigation system with open MRI for breast-conserving surgery. J Am Coll Surg 210(6):927-933
  • 72. Tsutsumi N et al (2013) Image-guided laparoscopic surgery in an open MRI operating theater. Surg Endosc 27(6):2178-2184
  • 73. Doi K (2007) Computer-aided diagnosis in medical imaging: historical review, current status and future potential. Comput Med Imaging Graph 31(4):198-211
  • 74. Ayache N (1995) Medical computer vision, virtual reality and robotics. Image Vis Comput 13(4):295-313
  • 75. Rangayyan RM, Ayres FJ, Desautels JL (2007) A review of computer-aided diagnosis of breast cancer: toward the detection of subtle signs. J Frankl Inst 344(3):312-348
  • 76. Shiraishi J et al (2007) Development of a computer-aided diagnostic scheme for detection of interval changes in successive whole-body bone scans. Med Phys 34(1):25-36
  • 77. Marr D (1976) Early processing of visual information. Philos Trans R Soc Lond B Biol Sci 275(942):483-519
  • 78. Poggio T, Torre V, Koch C (1989) Computational vision and regularization theory. Image Underst 3(1—18): 111
  • 79. Roberts LG (1963) Machine perception of three-dimensional soups. Massachusetts Institute of Technology
  • 80. Slaney J, Thiebaux S (2001) Blocks world revisited. Artif Intell 125(1): 119—153
  • 81. Binford TO (1971) Visual perception by computer. In: IEEE conference on systems and control
  • 82. Ponce J, Chelberg D, Mann WB (1989) Invariant properties of straight homogeneous generalized cylinders and their contours. IEEE Trans Pattern Anal Mach Intell 11(9):951- 966
  • 83. Marroquin J, Mitter S, Poggio T (1987) Probabilistic solution of ill-posed problems in computational vision. J Am Stat Assoc 82(397):76-89
  • 84. Fischler MA, Elschlager RA (1973) The representation and matching of pictorial structures. IEEE Trans Comput 22(1):67-92
  • 85. Felzenszwalb PF, Huttenlocher DP (2005) Pictorial structures for object recognition. Int J Comput Vis 61(1):55-79
  • 86. Terzopoulos D (1986) Regularization of inverse visual problems involving discontinuities. IEEE Trans Pattern Anal Mach Intell PAMI-8(4):413-424
  • 87. Cootes TF et al (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38-59
  • 88. Marr D, Hildreth E (1980) Theory of edge detection. Proc R Soc Lond Ser B 207(1167):187- 217
  • 89. Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679-698
  • 90. Kimmel R, Bruckstein AM (2003) Regularized Laplacian zero crossings as optimal edge integrators. Int J Comput Vis 53(3):225-243
  • 91. Cootes TF et al (1993) The use of active shape models for locating structures in medical images. In: Information processing in medical imaging. Springer, Berlin
  • 92. Audette MA, Ferrie FP, Peters TM (2000) An algorithmic overview of surface registration techniques for medical imaging. Med Image Anal 4(3):201-217
  • 93. Heimann T, Meinzer H-P (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543-563
  • 94. Hontani H, Tsunekawa Y, Sawada Y (2013) Accurate and robust registration of nonrigid surface using hierarchical statistical shape model. In: Computer Vision and Pattern Recognition (CVPR), 2013 IEEE Conference on. 2013. IEEE
  • 95. Davies RH et al (2010) Building 3-d statistical shape models by direct optimization. IEEE Trans Med Imaging 29(4):961-981
  • 96. Cross GR, Jain AK (1983) Markov random field texture models. IEEE Trans Pattern Anal Mach Intell 5(1):25-39
  • 97. Geman S, Geman D (1984) Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell 6(6):721-741
  • 98. Derin H, Elliott H (1987) Modeling and segmentation of noisy and textured images using Gibbs random fields. IEEE Trans Pattern Anal Mach Intell 9(1):39-55
  • 99. Adelson EH, Bergen JR (1991) The plenoptic function and the elements of early vision. Comput Model Vis Process 1(2):3-20
  • 100. Felzenszwalb PF, Huttenlocher DP (2006) Efficient belief propagation for early vision. Int J Comput Vis 70(1):41-54
  • 101. Szeliski R et al (2008) A comparative study of energy minimization methods for markov random fields with smoothness-based priors. IEEE Trans Pattern Anal Mach Intell 30(6):1068- 1080
  • 102. Blake A, Zisserman A (1987) Visual reconstruction, vol 2. MIT press, Cambridge
  • 103. Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42(5):577-685
  • 104. Prince SJ (2012) Computer vision: models, learning, and inference. Cambridge University Press, New York
  • 105. Hinton G, Osindero S, Teh Y-W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527-1554
  • 106. Bengio Y (2009) Learning deep architectures for AI. Found Trends® Mach Learn 2(1): 1-127
  • 107. Muller H et al (2004) A review of content-based image retrieval systems in medical applications—clinical benefits and future directions. Int J Med Inform 73(1): 1-23
  • 108. Sagerer G, Niemann H (2013) Semantic networks for understanding scenes. Springer, New York
  • 109. Tenenbaum JM, Witkin A (1983) On the role of structure in vision. In: Human and machine vision. Academic, New York, pp 481-543
  • 110. Spitzer V et al (1996) The visible human male: a technical report. J Am Med Inform Assoc 3(2):118-130
  • 111. Pommert A et al (2001) Creating a high-resolution spatial/symbolic model of the inner organs based on the Visible Human. Med Image Anal 5(3):221-228
  • 112. Beinfeld MT, Wittenberg E, Gazelle GS (2005) Cost-effectiveness of Whole-Body CT Screening 1. Radiology 234(2):415-422
  • 113. Antoch G et al (2003) Whole-body dual-modality PET/CT and whole-body MRI for tumor staging in oncology. JAMA 290(24):3199-3206
  • 114. Huber-Wagner S et al (2009) Effect of whole-body CT during trauma resuscitation on survival: a retrospective, multicentre study. Lancet 373(9673):1455-1461
  • 115. Takahara T et al (2003) Diffusion weighted whole body imaging with background body signal suppression (DWIBS): technical improvement using free breathing, STIR and high resolution 3D display. Radiat Med 22(4):275-282
  • 116. Yamashita T, Kwee TC, Takahara T (2009) Whole-body magnetic resonance neurography. N Engl J Med 361(5):538-539
  • 117. Silva AC et al (2010) Innovations in CT dose reduction strategy: application of the adaptive statistical iterative reconstruction algorithm. Am J Roentgenol 194(1): 191-199
  • 118. Turk M, Pentland A (1991) Eigenfaces for recognition. J Cogn Neurosci 3(1):71-86
  • 119. Blanz V,Vetter T (1999) A morphable model for the synthesis of 3D faces. In: Proceedings of the 26th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., New York
  • 120. Hisley KC et al (2008) Coupled physical and digital cadaver dissection followed by a visual test protocol provides insights into the nature of anatomical knowledge and its evaluation. Anat Sci Educ 1(1):27-40
  • 121. Jacobson S et al (2009) Creation of virtual patients from CT images of cadavers to enhance integration of clinical and basic science student learning in anatomy. Med Teach 31(8):749- 751
  • 122. Hunter PJ, Borg TK (2003) Integration from proteins to organs: the Physiome Project. Nat Rev Mol Cell Biol 4(3):237-243
  • 123. Delp SL et al (2007) OpenSim: open-source software to create and analyze dynamic simulations of movement. IEEE Trans Biomed Eng 54(11):1940-1950
  • 124. Xiao N, Humphrey JD, Figueroa CA (2013) Multi-scale computational model of threedimensional hemodynamics within a deformable full-body arterial network. J Comput Phys 244:22-40
  • 125. Nagaoka T et al (2004) Development of realistic high-resolution whole-body voxel models of Japanese adult males and females of average height and weight, and application of models to radio-frequency electromagnetic-field dosimetry. Phys Med Biol 49(1): 1
  • 126. Caon M (2004) Voxel-based computational models of real human anatomy: a review. Radiat Environ Biophys 42(4):229-235
  • 127. Christ A et al (2010) The virtual family—development of surface-based anatomical models of two adults and two children for dosimetric simulations. Phys Med Biol 55(2):N23
  • 128. Lee SL et al (2011) A whole body statistical shape model for radio frequency simulation. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE. 2011. IEEE
  • 129. Couinaud C (1957) Le foie: etudes anatomiques et chirurgicales. Masson & Cie, Paris
  • 130. Bismuth H (1982) Surgical anatomy and anatomical surgery of the liver. World J Surg 6(1): 3-9
  • 131. Castiglione F et al (2013) The onset of type 2 diabetes: proposal for a multi-scale model. JMIR Res Protoc 2(2):e44
 
Source
< Prev   CONTENTS   Source   Next >