Skip to Main content Skip to Navigation
Conference papers

A Boosted Segmentation Method for Surgical Workflow Analysis

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 work- flow 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.
Document type :
Conference papers
Complete list of metadata

Cited literature [15 references]  Display  Hide  Download

https://hal.inria.fr/inria-00177010
Contributor : Nicolas Padoy <>
Submitted on : Thursday, September 23, 2010 - 9:32:11 AM
Last modification on : Friday, February 26, 2021 - 3:28:08 PM
Long-term archiving on: : Friday, December 24, 2010 - 2:16:15 AM

File

padoy2007miccai.pdf
Files produced by the author(s)

Identifiers

Collections

Citation

Nicolas Padoy, Tobias Blum, Irfan Essa, Hubertus Feussner, Marie-Odile Berger, et al.. A Boosted Segmentation Method for Surgical Workflow Analysis. 10th International Conference on Medical Image Computing and Computer Assisted Intervention - MICCAI 2007, Oct 2007, Brisbane, Australia. pp.102-109, ⟨10.1007/978-3-540-75757-3_13⟩. ⟨inria-00177010⟩

Share

Metrics

Record views

443

Files downloads

482