Abstract
Modeling and analyzing surgeries based on signals that are obtained automatically from the operating room (OR) is a field of recent interest. It can be valuable for analyzing and understanding surgical workflow, for skills evaluation and developing context-aware ORs. In minimally invasive surgery, laparoscopic video is easy to record but it is challenging to extract meaningful information from it. We propose a method that uses additional information about tool usage to perform a dimensionality reduction on image features. Using Canonical Correlation Analysis (CCA) a projection of a high-dimensional image feature space to a low dimensional space is obtained such that semantic information is extracted from the video. To model a surgery based on the signals in the reduced feature space two different statistical models are compared. The capability of segmenting a new surgery into phases only based on the video is evaluated. Dynamic Time Warping which strongly depends on the temporal order in combination with CCA shows the best results.
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Keywords
- Hide Markov Model
- Dimensionality Reduction
- Semantic Information
- Minimally Invasive Surgery
- Canonical Correlation Analysis
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Blum, T., Feußner, H., Navab, N. (2010). Modeling and Segmentation of Surgical Workflow from Laparoscopic Video. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2010. MICCAI 2010. Lecture Notes in Computer Science, vol 6363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15711-0_50
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DOI: https://doi.org/10.1007/978-3-642-15711-0_50
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