Information Processing in Computer-Assisted Interventions: 5th International Conference, IPCAI 2014, Fukuoka, Japan, June 28, 2014 Proceedings (Lecture Notes in Computer Science)
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This book constitutes the refereed proceedings of the 5th International Conference on Information Processing in Computer-Assisted Interventions, IPCAI 2014, held in Fukuoka, Japan, on June 28, 2014. The 28 papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on planning, simulation, patient specific models for computer assisted interventions, medical robotics and surgical navigation, interventional imaging and advanced intra-op visualization, cognition, modeling and context awareness, clinical applications, systems, software, and validation.
Terzopoulos University of California, Los Angeles, CA, USA Doug Tygar University of California, Berkeley, CA, USA Gerhard Weikum Max Planck Institute for Informatics, Saarbruecken, Germany 8498 Danail Stoyanov D. Louis Collins Ichiro Sakuma Purang Abolmaesumi Pierre Jannin (Eds.) Information Processing in Computer-Assisted Interventions 5th International Conference, IPCAI 2014 Fukuoka, Japan, June 28, 2014 Proceedings 13 Volume Editors Danail Stoyanov University College London, UK E-mail:
position in relation to a three-dimensional exponential decay centered at the sample position sj,x , sj,y , sj,z of the current scanline ray sample j K(vi , sj ) = 1 3 2 (2π) |B| sj e− 2 (vi 1 1 2 s −sj )T B −1 (vi j −sj ) , B = diag(σx2 , σy2 , σz2 ). (6) The spatial variances are set to σx = 12 dmx , σy = 12 dmy , σz = 12 dmz to assure the ellipsoid cut-oﬀ at 2σ (95.4%). By specifying these distances based on the physical properties, only samples fullﬁlling these prior information
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among all annotators, including and excluding the classiﬁer, along with its 95% conﬁdence interval (95% CI). We examined whether the Fleiss’ kappa including the classiﬁer was consistent with the 95% CI for the kappa computed after excluding the classiﬁer. We used only pairs of maneuvers that the individuals marked as being conﬁdent about their preference. We assessed validity of preferences assigned by the classiﬁer by computing the accuracy compared against AC for each member in the crowd. We