IdpMichaelTautschnig

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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IDP Online Workflow Recovery

Literature

1. K. Cleary, H. Y. Chung, and S. K. Mun. OR 2020: The Operating Room of the Future. Laparoendoscopic and Advanced Surgical Techniques, 15(5):495–500, 2005. 2. D. Gusfield. Algorithms on Strings, Trees, and Sequences. Cambridge University Press, 1997. 3. A. Kassidas, J. F. MacGregor?, and P. A. Taylor. Synchronization of batch trajectories using dynamic time warping. AIChE? Journal, 44(4):863–875, 1998. 4. H. C. Lin, I. Shafran, T. E. Murphy, A. M. Okamura, D. D. Yuh, and G. D. Hager. Automatic Detection and Segmentation of Robot-Assisted Surgical Motions. In J. S. Duncan and G. Gerig, editors, MICCAI, volume 3749 of Lecture Notes in Computer Science, pages 802–810. Springer, 2005. 5. J. Rosen, M. Solazzo, B. Hannaford, and M. Sinanan. Task decomposition of laparoscopic surgery for objective evaluation of surgical residents’ learning curve using hidden Markov model. Computer Aided Surgery, 7(1):49–61, 2002. 6. H. Sakoe and S. Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE Transactions on Acoustics, Speech, and Signal Processing, 26(1):43–49, 1978. 7. T. Sielhorst, T. Blum, and N. Navab. Synchronizing 3D Movements for Quantitative Comparison and Simultaneous Visualization of Actions. In Fourth IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR’05), pages 38– 47, 2005. 8. R. A. Wagner and M. J. Fischer. The String-to-String Correction Problem. Journal of the ACM, 21(1):168–173, 1974. 9. K. Wang and T. Gasser. Alignment of curves by dynamic time warping. Annals of Statistics, 25(3):1251–1276, 1997. 16

Students.ProjectForm
Title: Online Workflow Recovery
Abstract: Workflow recovery deals with the problem of identifying related phases of two recorded processes, given one of them has been annotated as desired. Once the relation has been established, the information can be used for, e. g., documentation purposes or process optimization. On the other hand, training feedback can be given by synchronizing a trainee’s 3D hand movements to those of an expert surgeon [7]. In the latter case, dynamic time warping (DTW) has been used, which has also been applied successfully in the context of statistics, speech recognition or error detection in industrial processes. Our motivation stems from the analysis of surgical operations. The offline recovery of the workflow allows for, e. g., automated documentation, but also for general OR workflow optimization. The importance of this issue in the OR of the future has been underlined in the OR2020 workshop. Further work on this subject is based on hidden Markov models to analyze the skills of the surgeon. However, their focus is on extraction and analysis of single tasks, whereas here the analysis of the whole process, i. e., the complete surgery, shall be emphasized.
Student: Michael Tautschnig
Director: Prof. Lasser
Supervisor: Tobias Sielhorst
Type: IDP
Status: finished
Start: 2006/11/26
Finish: 2006/04/26


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