Modeling and segmentation of surgical workflow from laparoscopic video.
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.
Blum
Tobias
T
Computer Aided Medical Procedures, Technische Universitiät München, Germany.
Feussner
Hubertus
H
Navab
Nassir
N
D003198:Computer Simulation; D005858:Germany; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010535:Laparoscopy / Q000379:methods*; D008962:Models, Theoretical; D011361:Professional Competence; D013647:Task Performance and Analysis; D014741:Video Recording / Q000379:methods*; D057188:Workflow