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Authors: Jurgen Fripp, Stuart Crozier, Simon K Warfield, S´ebastien Ourselin

Automatic segmentation of the bone and extraction of the bone–cartilage interface from magnetic resonance images of the knee. Authors: Jurgen Fripp, Stuart Crozier, Simon K Warfield, S´ebastien Ourselin Source: PHYSICS IN MEDICINE AND BIOLOGY 52 (2007) 1617–1631 Speaker: Yi-Chun Ke

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Authors: Jurgen Fripp, Stuart Crozier, Simon K Warfield, S´ebastien Ourselin

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  1. Automatic segmentation of the bone and extraction of the bone–cartilage interface from magnetic resonance images of the knee Authors:Jurgen Fripp, Stuart Crozier, Simon K Warfield, S´ebastien Ourselin Source:PHYSICS IN MEDICINE AND BIOLOGY 52 (2007) 1617–1631 Speaker:Yi-Chun Ke Adviser:Bo-Chi Lai Ku-Yaw Chang

  2. Outline • Introduction • Method • Results • Conclusion

  3. femur patella Introduction muscle tissue • Osteoarthritis(OA) • Characterized • changes in the structure • degeneration of the cartilage tissue • Magnetic Resonance Imaging(MRI ) • non-invasively adipose tissue tibia

  4. Method • point distribution model (PDM) • 3D-statistical shape models (3D-SSM) • 3D-active shape model(3D-ASM) • Dice similarity coefficient(DSC)

  5. Method • point distribution model (PDM) • 3D-statistical shape models (3D-SSM) • 3D-active shape model(3D-ASM) • Dice similarity coefficient(DSC)

  6. Method • point distribution model (PDM) • Landmarking and obtaining correspondence • Representthe shape of the bones

  7. Method • point distribution model (PDM) • 3D-statistical shape models (3D-SSM) • 3D-active shape model(3D-ASM) • Dice similarity coefficient(DSC)

  8. Method • 3D-statistical shape models (3D-SSM) • estimate the pose and shape • individual SSM of each of the bones (patella, tibia and femur) • combined knee SSM Affine

  9. Method • point distribution model (PDM) • 3D-statistical shape models (3D-SSM) • 3D-active shape model(3D-ASM) • Dice similarity coefficient(DSC)

  10. Method • 3D-active shape model(3D-ASM) • two components • SSM • matching criteria (often an appearance model) • two steps • Deformation • Shape restriction

  11. Method • shape constraints obtained from the small database are overly restrictive • Disabling shape constraints • Replacing • BCI > 95% relax 3D ASM

  12. Method • point distribution model (PDM) • 3D-statistical shape models (3D-SSM) • 3D-active shape model(3D-ASM) • Dice similarity coefficient(DSC)

  13. Method • Dice similarity coefficient(DSC) • sensitivity = TP/(TP + FN) • specificity = TN/(TN + FP) • Dice similarity coefficient = 2TP/(2TP + FP + FN) Tn Tp Fp Fn 實驗結果 標準答案

  14. Results

  15. Results

  16. Conclusion • automatically segment the bones andaccurately extract the BCI • each segmentation taking approximately 20 min • DSC of 0.96,0.96 and 0.89 (femur, tibia and patella) • only 3.60% failing • approach for automatic use in subsequentcartilage segmentation and quantitative analysis

  17. 參考文獻 • 吳政容,張顧耀,私立大葉大學資訊工程學系,以輻射轉換為基礎之膝關節軟骨分割,2009 • 陳彥菁,黃聖方,私立慈濟大學醫徐資訊研究所,應用類神經網路在三維乳房超音波影像腫瘤之切割,2008 • Jurgen Fripp, Stuart Crozier, Simon K Warfield, S´ebastien Ourselin, PHYSICS IN MEDICINE AND BIOLOGY,52,2007,1617–1631 • MRI原理 • http://www.cbshow.org.tw/internet/activity/mri/ • http://www2.cmu.edu.tw/~anatomy/ctanatomy/MRI/index.html • 膝蓋軟骨軟化症（髕骨軟骨軟化症） • http://www.mobile01.com/topicdetail.php?f=311&t=468552 • Shape Model Formulation • http://www2.imm.dtu.dk/%7Eaam/main/node12.html

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