MITK-based segmentation of co-registered MRI for subject-related regional anesthesia simulation

Christian Teich; Wei Liao; Sebastian Ullrich; Torsten Kuhlen; Alexandre Ntouba M.D.; Rolf Rossaint M.D.; Marcus Ullisch; Thomas M. Deserno

doi:10.1117/12.771106

17 March 2008 MITK-based segmentation of co-registered MRI for subject-related regional anesthesia simulation

Christian Teich, Wei Liao, Sebastian Ullrich, Torsten Kuhlen, Alexandre Ntouba M.D., Rolf Rossaint M.D., Marcus Ullisch, Thomas M. Deserno

Author Affiliations +

Proceedings Volume 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling; 69182M (2008) https://doi.org/10.1117/12.771106
Event: Medical Imaging, 2008, San Diego, California, United States

Abstract

With a steadily increasing indication, regional anesthesia is still trained directly on the patient. To develop a virtual reality (VR)-based simulation, a patient model is needed containing several tissues, which have to be extracted from individual magnet resonance imaging (MRI) volume datasets. Due to the given modality and the different characteristics of the single tissues, an adequate segmentation can only be achieved by using a combination of segmentation algorithms. In this paper, we present a framework for creating an individual model from MRI scans of the patient. Our work splits in two parts. At first, an easy-to-use and extensible tool for handling the segmentation task on arbitrary datasets is provided. The key idea is to let the user create a segmentation for the given subject by running different processing steps in a purposive order and store them in a segmentation script for reuse on new datasets. For data handling and visualization, we utilize the Medical Imaging Interaction Toolkit (MITK), which is based on the Visualization Toolkit (VTK) and the Insight Segmentation and Registration Toolkit (ITK). The second part is to find suitable segmentation algorithms and respectively parameters for differentiating the tissues required by the RA simulation. For this purpose, a fuzzy c-means clustering algorithm combined with mathematical morphology operators and a geometric active contour-based approach is chosen. The segmentation process itself aims at operating with minimal user interaction, and the gained model fits the requirements of the simulation. First results are shown for both, male and female MRI of the pelvis.

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Christian Teich, Wei Liao, Sebastian Ullrich, Torsten Kuhlen, Alexandre Ntouba M.D., Rolf Rossaint M.D., Marcus Ullisch, and Thomas M. Deserno "MITK-based segmentation of co-registered MRI for subject-related regional anesthesia simulation", Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69182M (17 March 2008); https://doi.org/10.1117/12.771106

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