In Search of Robustness and Efficiency via l1− and l2− Regularized Optimization for Physiological Motion Compensation
Compensating physiological motion in the context
of minimally invasive cardiac surgery has become an attractive
issue since it outperforms traditional cardiac procedures offering
remarkable benefits. Owing to space restrictions, computer vision
techniques have proven to be the most practical and suitable solution.
However, the lack of robustness and efficiency of existing methods
make physiological motion compensation an open and challenging
problem. This work focusses on increasing robustness and efficiency
via exploration of the classes of 1−and 2−regularized optimization,
emphasizing the use of explicit regularization. Both approaches are
based on natural features of the heart using intensity information.
Results pointed out the 1−regularized optimization class as the best
since it offered the shortest computational cost, the smallest average
error and it proved to work even under complex deformations.
[1] J. Livesay, "The benefits of off-pump coronary bypass: A reality or an
illusion?” Texas Heart Institute Journal, pp. 258–260, 2003.
[2] V. Vitiello, K. Kwok, and G. Yang, "Introduction to robot-assisted
minimally invasive surgery,” Book Chapter, Medical Robotics Minimally
Invasive Surgery, 2012.
[3] M. L. Koransky, M. L. Tavana, A. Yamaguchi, M. H. Kown, D. N.
Miniati, W. Nowlin, and R. C. Robbins, "Quantification of mechanical
stabilization for the performance of off-pump coronary artery surgery,”
Heart Surg. Forum, vol. 6, pp. 224–231, 2003.
[4] A. Lemma, A. Mangini, A. Redaelli, and F. Acocella, "Do cardiac
stabilizers really stabilize? experimental quantitative analysis of
mechanical stabilization,” Interactive CardioVascular and Thoracic
Surgery, 2005.
[5] S. Atashzar, I. Khalaji, M. Shahbazi, A. Talasaz, R. Patel, and
M. Naish, "Robot-assisted lung motion compensation during needle
insertion,” IEEE International Conference on Robotics and Automation,
pp. 1682–1687, 2013.
[6] J. Gagne, W. Bachta, P. Renaud, P. O., Laroche, and J. Gangloff,
"Beating heart surgery: Comparison of two active compensation
solutions for minimally invasive coronary artery bypass grafting,” Book
Chapter Computational Surgery and Dual Training Computing, Robotics
and Imaging, pp. 203–210, 2014.
[7] L. Hoff, O. Elle, M. Grimnes, S. Halvorsen, H. Alker, and E. Fosse,
"Measurements of heart motion using accelerometers,” Proceedings
of the 26th Annual International Conference of the IEEE EMBS, pp.
2049–2051, 2004.
[8] M. Hayashibe, N. Suzuki, and Y. Nakamura, "Laser-scan endoscope
system for intraoperative geometry acquisition and surgical robot safety
management,” Journal in Medical Image Analysis, pp. 509–519, 2006.
[9] O. Bebek and M. Cavusoglu, "Whisker sensor design for three
dimensional position measurement in robotic assisted beating heart
surgery,” IEEE International Conference on Robotics and Automation,
pp. 225–231, 2007.
[10] P. Puangmali, H. Liu, K. Althoefer, and L. Seneviratne, "Optical
fiber sensor for soft tissue investigation during minimally invasive
surgery,” IEEE International Conference on Robotics and Automation,
pp. 2934–2939, 2008.
[11] W. Hu, H. Zhang, Z. Zhao, Y. Wang, and X. Wang, "Real-time remote
vital sign detection using a portable doppler sensor system,” IEEE
Sensors Applications Symposium (SAS), pp. 89–93, 2014.
[12] Y. Nakamura, K. Kishi, and H. Kawakami, "Heartbeat synchronization
for robotic cardiac surgery,” International Conference on Robotics and
Automation, pp. 2014 – 2019, 2001.
[13] M. Sauve, A. Noce, P. Poignet, J. Triboulet, and E. Dombre,
"Three-dimensional heart motion estimation using endoscopic
monocular vision system: From artificial landmarks to texture
analysis,” Biomedical Signal Processing and Control, pp. 199 – 207,
2007.
[14] E. Bogatyrenko, P. Pompey, and U. Hanebeck, "Efficient physics-based
tracking of heart surface motion for beating heart surgery robotic
systems,” International Journal of Computer Assisted Radiology and
Surgery, pp. 387–399, 2011.
[15] T. Ortmaier, M. Groger, D. Boehm, V. Falk, and G. Hirzinger, "Motion
estimation in beating heart surgery,” IEEE Transactions on Biomedical
Engineering, pp. 1729–1740, 2005.
[16] R. Richa, A. B, and P. Poignet, "Towards robust 3d visual tracking for
motion compensation in beating heart surgery,” Medical Image Analysis,
vol. 15, no. 3, pp. 302 – 315, 2011.
[17] H. Elhawary and A. Popovic, "Robust feature tracking on the beating
heart for a robotic-guided endoscope,” The International Journal of
Medical Robotics and computer Assisted Surgery, 2011.
[18] J. Hadamard, "Lectures on the cauchy problems in linear partial
differential equations,” Yale University Press, New Haven, 1923.
[19] A. Aviles and A. Casals, "Interpolation based deformation model
for minimally invasive beating heart surgery,” Book Chapter IFMBE
Proceedings vol.41, Springer International Publishing, 2013., 2013.
[20] A. Sotiras, C. Davatazikosy, and N. Paragios, "Deformable medical
image registration: A survey,” Research Report Num. 7919, INRIA,
France, 2012.
[21] M. Unser, A. Aldroubi, and M. Eden, "The l2-polynomial spline
pyramid,” IEEE Trans. Patter Anal. Mach. Intell., pp. 364–379, 1993.
[22] M. Unser, "Splines: A perfect fit for signal and image processing,” IEEE
Signal Processing Magazine, vol.16, no.6, pp. 22–38, 1999.
[23] A. N. Tikhonov and A. V. Y., "Solution of ill-posed problems,” Winston
and Sons, Washinton DC, 1977.
[24] L. Rudin, S. J. Osher, and E. Fatemi, "Nonlinear total variation based
noise removal algorithms,” Physica D., 60:259-268, 1992.
[25] A. Chambolle, M. Caselles, D. Cremers, and T. Pock, "An introduction
to total variation for image analysis,” Report number 00437581, pp.
1–87, 2009.
[26] N. Z. Shor, "Subgradient methods: A survey of soviet research
nonsmooth optimization,” Proceedings of the IIASA Workshop,
Lemar´echal and R. Mifflin eds. Pergamon Press, 1978.
[27] D. Bertsekas and S. Mitter, "A descent numerical method for
optimization problems with nondifferentiable cost functionals,” SlAM
Journal on Control, pp. 637–652, 1973.
[28] A. Cauchy, "M´ethode g´en´erale pour la r´esolution des syst`ems d
equations simultan´ees,” Comp. Rend. Sci. Paris, pp. 46–89, 1847.
[29] R. Fletcher and C. M. Reeves, "Function minimization by conjugate
gradients,” Computer Journal, pp. 149–154, 1964.
[30] A. Aviles and A. Casals, "On genetic algorithms optimization for
heart motion compensation,” Book Section ROBOT2013 Springer
International, 2014.
[31] K. Levenberg, "A method for the solution of certain non-linear problems
in least squares,” Quarterly of Applied Mathematics, 1944.
[32] D. Marquardt, "An algorithm for least-squares estimation of nonlinear
parameters,” SIAM Journal on Applied Mathematics, pp. 431–441, 1963.
[33] M. Powell, "A hybrid method for nonlinear equations,” Numerical
Methods for Nonlinear Algebraic Equations, pp. 87–144, 1970.
[34] D. Stoyanov, G. Mylonas, F. Deligianni, A. Darzi, and G. Yang,
"Soft-tissue motion tracking and structure estimation for robotic
assisted mis procedures,” In Proceeding Conference on Medical Image
Computing and Computer Assisted Intervention, pp. 139–146, 2005.
[35] A. Talasaz, A. Trejos, and R. Patel, "Effect of force feedback on
performance of robotics-assisted suturing,” International Conference on
Biomedical Robotics and Biomechatronics, pp. 823–828, 2012.
[36] P. Boonvisut and M. Cavusoglu, "Estimation of soft tissue mechanical
parameters from robotic manipulation data,” IEEE Transactions on
Mechatronics, pp. 1602–1611, 2013.
[37] B. Bickel, M. Bacher, M. Otaduy, W. Matusik, H. Pfister, and M. Gross,
"Capture and modeling of non-linear heterogeneous soft tissue,” ACM
Transactions on Graphics, 2009.
[38] A. Aviles, A. Marban, P. Sobrevilla, J. Fernandez, and A. Casals, "A
recurrent neural network approach for 3d vision-based force estimation,”
To appear in IEEE International Conference on Image Processing
Theory, Tools and Applications, 2014
[1] J. Livesay, "The benefits of off-pump coronary bypass: A reality or an
illusion?” Texas Heart Institute Journal, pp. 258–260, 2003.
[2] V. Vitiello, K. Kwok, and G. Yang, "Introduction to robot-assisted
minimally invasive surgery,” Book Chapter, Medical Robotics Minimally
Invasive Surgery, 2012.
[3] M. L. Koransky, M. L. Tavana, A. Yamaguchi, M. H. Kown, D. N.
Miniati, W. Nowlin, and R. C. Robbins, "Quantification of mechanical
stabilization for the performance of off-pump coronary artery surgery,”
Heart Surg. Forum, vol. 6, pp. 224–231, 2003.
[4] A. Lemma, A. Mangini, A. Redaelli, and F. Acocella, "Do cardiac
stabilizers really stabilize? experimental quantitative analysis of
mechanical stabilization,” Interactive CardioVascular and Thoracic
Surgery, 2005.
[5] S. Atashzar, I. Khalaji, M. Shahbazi, A. Talasaz, R. Patel, and
M. Naish, "Robot-assisted lung motion compensation during needle
insertion,” IEEE International Conference on Robotics and Automation,
pp. 1682–1687, 2013.
[6] J. Gagne, W. Bachta, P. Renaud, P. O., Laroche, and J. Gangloff,
"Beating heart surgery: Comparison of two active compensation
solutions for minimally invasive coronary artery bypass grafting,” Book
Chapter Computational Surgery and Dual Training Computing, Robotics
and Imaging, pp. 203–210, 2014.
[7] L. Hoff, O. Elle, M. Grimnes, S. Halvorsen, H. Alker, and E. Fosse,
"Measurements of heart motion using accelerometers,” Proceedings
of the 26th Annual International Conference of the IEEE EMBS, pp.
2049–2051, 2004.
[8] M. Hayashibe, N. Suzuki, and Y. Nakamura, "Laser-scan endoscope
system for intraoperative geometry acquisition and surgical robot safety
management,” Journal in Medical Image Analysis, pp. 509–519, 2006.
[9] O. Bebek and M. Cavusoglu, "Whisker sensor design for three
dimensional position measurement in robotic assisted beating heart
surgery,” IEEE International Conference on Robotics and Automation,
pp. 225–231, 2007.
[10] P. Puangmali, H. Liu, K. Althoefer, and L. Seneviratne, "Optical
fiber sensor for soft tissue investigation during minimally invasive
surgery,” IEEE International Conference on Robotics and Automation,
pp. 2934–2939, 2008.
[11] W. Hu, H. Zhang, Z. Zhao, Y. Wang, and X. Wang, "Real-time remote
vital sign detection using a portable doppler sensor system,” IEEE
Sensors Applications Symposium (SAS), pp. 89–93, 2014.
[12] Y. Nakamura, K. Kishi, and H. Kawakami, "Heartbeat synchronization
for robotic cardiac surgery,” International Conference on Robotics and
Automation, pp. 2014 – 2019, 2001.
[13] M. Sauve, A. Noce, P. Poignet, J. Triboulet, and E. Dombre,
"Three-dimensional heart motion estimation using endoscopic
monocular vision system: From artificial landmarks to texture
analysis,” Biomedical Signal Processing and Control, pp. 199 – 207,
2007.
[14] E. Bogatyrenko, P. Pompey, and U. Hanebeck, "Efficient physics-based
tracking of heart surface motion for beating heart surgery robotic
systems,” International Journal of Computer Assisted Radiology and
Surgery, pp. 387–399, 2011.
[15] T. Ortmaier, M. Groger, D. Boehm, V. Falk, and G. Hirzinger, "Motion
estimation in beating heart surgery,” IEEE Transactions on Biomedical
Engineering, pp. 1729–1740, 2005.
[16] R. Richa, A. B, and P. Poignet, "Towards robust 3d visual tracking for
motion compensation in beating heart surgery,” Medical Image Analysis,
vol. 15, no. 3, pp. 302 – 315, 2011.
[17] H. Elhawary and A. Popovic, "Robust feature tracking on the beating
heart for a robotic-guided endoscope,” The International Journal of
Medical Robotics and computer Assisted Surgery, 2011.
[18] J. Hadamard, "Lectures on the cauchy problems in linear partial
differential equations,” Yale University Press, New Haven, 1923.
[19] A. Aviles and A. Casals, "Interpolation based deformation model
for minimally invasive beating heart surgery,” Book Chapter IFMBE
Proceedings vol.41, Springer International Publishing, 2013., 2013.
[20] A. Sotiras, C. Davatazikosy, and N. Paragios, "Deformable medical
image registration: A survey,” Research Report Num. 7919, INRIA,
France, 2012.
[21] M. Unser, A. Aldroubi, and M. Eden, "The l2-polynomial spline
pyramid,” IEEE Trans. Patter Anal. Mach. Intell., pp. 364–379, 1993.
[22] M. Unser, "Splines: A perfect fit for signal and image processing,” IEEE
Signal Processing Magazine, vol.16, no.6, pp. 22–38, 1999.
[23] A. N. Tikhonov and A. V. Y., "Solution of ill-posed problems,” Winston
and Sons, Washinton DC, 1977.
[24] L. Rudin, S. J. Osher, and E. Fatemi, "Nonlinear total variation based
noise removal algorithms,” Physica D., 60:259-268, 1992.
[25] A. Chambolle, M. Caselles, D. Cremers, and T. Pock, "An introduction
to total variation for image analysis,” Report number 00437581, pp.
1–87, 2009.
[26] N. Z. Shor, "Subgradient methods: A survey of soviet research
nonsmooth optimization,” Proceedings of the IIASA Workshop,
Lemar´echal and R. Mifflin eds. Pergamon Press, 1978.
[27] D. Bertsekas and S. Mitter, "A descent numerical method for
optimization problems with nondifferentiable cost functionals,” SlAM
Journal on Control, pp. 637–652, 1973.
[28] A. Cauchy, "M´ethode g´en´erale pour la r´esolution des syst`ems d
equations simultan´ees,” Comp. Rend. Sci. Paris, pp. 46–89, 1847.
[29] R. Fletcher and C. M. Reeves, "Function minimization by conjugate
gradients,” Computer Journal, pp. 149–154, 1964.
[30] A. Aviles and A. Casals, "On genetic algorithms optimization for
heart motion compensation,” Book Section ROBOT2013 Springer
International, 2014.
[31] K. Levenberg, "A method for the solution of certain non-linear problems
in least squares,” Quarterly of Applied Mathematics, 1944.
[32] D. Marquardt, "An algorithm for least-squares estimation of nonlinear
parameters,” SIAM Journal on Applied Mathematics, pp. 431–441, 1963.
[33] M. Powell, "A hybrid method for nonlinear equations,” Numerical
Methods for Nonlinear Algebraic Equations, pp. 87–144, 1970.
[34] D. Stoyanov, G. Mylonas, F. Deligianni, A. Darzi, and G. Yang,
"Soft-tissue motion tracking and structure estimation for robotic
assisted mis procedures,” In Proceeding Conference on Medical Image
Computing and Computer Assisted Intervention, pp. 139–146, 2005.
[35] A. Talasaz, A. Trejos, and R. Patel, "Effect of force feedback on
performance of robotics-assisted suturing,” International Conference on
Biomedical Robotics and Biomechatronics, pp. 823–828, 2012.
[36] P. Boonvisut and M. Cavusoglu, "Estimation of soft tissue mechanical
parameters from robotic manipulation data,” IEEE Transactions on
Mechatronics, pp. 1602–1611, 2013.
[37] B. Bickel, M. Bacher, M. Otaduy, W. Matusik, H. Pfister, and M. Gross,
"Capture and modeling of non-linear heterogeneous soft tissue,” ACM
Transactions on Graphics, 2009.
[38] A. Aviles, A. Marban, P. Sobrevilla, J. Fernandez, and A. Casals, "A
recurrent neural network approach for 3d vision-based force estimation,”
To appear in IEEE International Conference on Image Processing
Theory, Tools and Applications, 2014
@article{"International Journal of Medical, Medicine and Health Sciences:52079", author = "Angelica I. Aviles and Pilar Sobrevilla and Alicia Casals", title = "In Search of Robustness and Efficiency via l1− and l2− Regularized Optimization for Physiological Motion Compensation", abstract = "Compensating physiological motion in the context
of minimally invasive cardiac surgery has become an attractive
issue since it outperforms traditional cardiac procedures offering
remarkable benefits. Owing to space restrictions, computer vision
techniques have proven to be the most practical and suitable solution.
However, the lack of robustness and efficiency of existing methods
make physiological motion compensation an open and challenging
problem. This work focusses on increasing robustness and efficiency
via exploration of the classes of 1−and 2−regularized optimization,
emphasizing the use of explicit regularization. Both approaches are
based on natural features of the heart using intensity information.
Results pointed out the 1−regularized optimization class as the best
since it offered the shortest computational cost, the smallest average
error and it proved to work even under complex deformations.
", keywords = "Motion Compensation, Optimization, Regularization,
Beating Heart Surgery, Ill-posed problem.", volume = "8", number = "9", pages = "548-6", }
