TY - GEN
T1 - Surgical gesture segmentation and recognition
AU - Tao, Lingling
AU - Zappella, Luca
AU - Hager, Gregory D.
AU - Vidal, René
PY - 2013
Y1 - 2013
N2 - Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.
AB - Automatic surgical gesture segmentation and recognition can provide useful feedback for surgical training in robotic surgery. Most prior work in this field relies on the robot's kinematic data. Although recent work [1,2] shows that the robot's video data can be equally effective for surgical gesture recognition, the segmentation of the video into gestures is assumed to be known. In this paper, we propose a framework for joint segmentation and recognition of surgical gestures from kinematic and video data. Unlike prior work that relies on either frame-level kinematic cues, or segment-level kinematic or video cues, our approach exploits both cues by using a combined Markov/semi-Markov conditional random field (MsM-CRF) model. Our experiments show that the proposed model improves over a Markov or semi-Markov CRF when using video data alone, gives results that are comparable to state-of-the-art methods on kinematic data alone, and improves over state-of-the-art methods when combining kinematic and video data.
KW - conditional random fields
KW - structured output learning
KW - surgical gesture recognition
KW - surgical gesture segmentation
KW - time series analysis
UR - http://www.scopus.com/inward/record.url?scp=84894606700&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894606700&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40760-4_43
DO - 10.1007/978-3-642-40760-4_43
M3 - Conference contribution
C2 - 24505779
AN - SCOPUS:84894606700
SN - 9783642407598
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 339
EP - 346
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013 - 16th International Conference, Proceedings
T2 - 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
Y2 - 22 September 2013 through 26 September 2013
ER -