Paper MICCAI (2007): “A Boosted Segmentation Method for Surgical Workflow Analysis”

  • N. Padoy, T. Blum, I. Essa, H. Feussner, M. O. Berger, and N. Navab (2007), “A Boosted Segmentation Method for Surgical Workflow Analysis,” in Proceedings of International Conference on Medical Imaging Computing and Computer Assisted Intervention, (MICCAI), Brisbane, Australia, 2007. [PDF] [DOI] [BIBTEX]
    @InProceedings{    2007-Padoy-BSMSWA,
      address  = {Brisbane, Australia},
      author  = {N. Padoy and T. Blum and I. Essa and H. Feussner
          and M. O. Berger and N. Navab},
      booktitle  = {Proceedings of International Conference on Medical
          Imaging Computing and Computer Assisted
          Intervention, (MICCAI)},
      doi    = {10.1007/978-3-540-75757-3_13},
      pdf    = {http://www.cc.gatech.edu/~irfan/p/2007-Padoy-BSMSWA.pdf},
      publisher  = {Springer Lecture Notes in Computer Science (LNCS)
          series},
      title    = {A Boosted Segmentation Method for Surgical Workflow
          Analysis},
      year    = {2007}
    }

Abstract

As demands on hospital efficiency increase, there is a stronger need for automatic analysis, recovery, and modification of surgical workflows. Even though most of the previous work has dealt with higher level and hospital-wide workflow including issues like document management, workflow is also an important issue within the surgery room. Its study has a high potential, e.g., for building context-sensitive operating rooms, evaluating and training surgical staff, optimizing surgeries and generating automatic reports.In this paper, we propose an approach to segment the surgical workflow into phases based on temporal synchronization of multidimensional state vectors. Our method is evaluated on the example of laparoscopic cholecystectomy with state vectors representing tool usage during the surgeries. The discriminative power of each instrument in regard to each phase is estimated using AdaBoost. A boosted version of the Dynamic Time Warping DTW algorithm is used to create a surgical reference model and to segment a newly observed surgery. Full cross-validation on ten surgeries is performed and the method is compared to standard DTW and to Hidden Markov Models.

At the 10th International Conference on Medical Image Computing and Computer Assisted Intervention, 29 October to 2 November 2007 in Brisbane, Australia.

via Abstract – SpringerLink.

Tags: , , , , , | Categories: Activity Recognition, Health Systems, Medical, MICCAI | Date: October 21st, 2007 | By: Irfan Essa |

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