Andre Mastmeyer1, Dirk Fortmeier2, Heinz Handels3. 1. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany. Electronic address: mastmeyer@imi.uni-luebeck.de. 2. Institute of Medical Informatics and the Graduate School for Computing in Medicine and Life Sciences, University of Lübeck, Lübeck, Germany. 3. Institute of Medical Informatics, University of Lübeck, Lübeck, Germany.
Abstract
BACKGROUND AND OBJECTIVE: This work presents a new time-saving virtual patient modeling system by way of example for an existing visuo-haptic training and planning virtual reality (VR) system for percutaneous transhepatic cholangio-drainage (PTCD). METHODS: Our modeling process is based on a generic patient atlas to start with. It is defined by organ-specific optimized models, method modules and parameters, i.e. mainly individual segmentation masks, transfer functions to fill the gaps between the masks and intensity image data. In this contribution, we show how generic patient atlases can be generalized to new patient data. The methodology consists of patient-specific, locally-adaptive transfer functions and dedicated modeling methods such as multi-atlas segmentation, vessel filtering and spline-modeling. RESULTS: Our full image volume segmentation algorithm yields median DICE coefficients of 0.98, 0.93, 0.82, 0.74, 0.51 and 0.48 regarding soft-tissue, liver, bone, skin, blood and bile vessels for ten test patients and three selected reference patients. Compared to standard slice-wise manual contouring time saving is remarkable. CONCLUSIONS: Our segmentation process shows out efficiency and robustness for upper abdominal puncture simulation systems. This marks a significant step toward establishing patient-specific training and hands-on planning systems in a clinical environment.
BACKGROUND AND OBJECTIVE: This work presents a new time-saving virtual patient modeling system by way of example for an existing visuo-haptic training and planning virtual reality (VR) system for percutaneous transhepatic cholangio-drainage (PTCD). METHODS: Our modeling process is based on a generic patient atlas to start with. It is defined by organ-specific optimized models, method modules and parameters, i.e. mainly individual segmentation masks, transfer functions to fill the gaps between the masks and intensity image data. In this contribution, we show how generic patient atlases can be generalized to new patient data. The methodology consists of patient-specific, locally-adaptive transfer functions and dedicated modeling methods such as multi-atlas segmentation, vessel filtering and spline-modeling. RESULTS: Our full image volume segmentation algorithm yields median DICE coefficients of 0.98, 0.93, 0.82, 0.74, 0.51 and 0.48 regarding soft-tissue, liver, bone, skin, blood and bile vessels for ten test patients and three selected reference patients. Compared to standard slice-wise manual contouring time saving is remarkable. CONCLUSIONS: Our segmentation process shows out efficiency and robustness for upper abdominal puncture simulation systems. This marks a significant step toward establishing patient-specific training and hands-on planning systems in a clinical environment.
Authors: Paolo Zaffino; Guillaume Pernelle; Andre Mastmeyer; Alireza Mehrtash; Hongtao Zhang; Ron Kikinis; Tina Kapur; Maria Francesca Spadea Journal: Phys Med Biol Date: 2019-08-14 Impact factor: 3.609