17 March 2008 Atlas-based segmentation of deep brain structures using non-rigid registration
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Deep brain structures are frequently used as targets in neurosurgical procedures. However, the boundaries of these structures are often not visible in clinically used MR and CT images. Techniques based on anatomical atlases and indirect targeting are used to infer the location of these targets intraoperatively. Initial errors of such approaches may be up to a few millimeters, which is not negligible. E.g. subthalamic nucleus is approximately 4x6 mm in the axial plane and the diameter of globus pallidus internus is approximately 8 mm, both of which are used as targets in deep brain stimulation surgery. To increase the initial localization accuracy of deep brain structures we have developed an atlas-based segmentation method that can be used for the surgery planning. The atlas is a high resolution MR head scan of a healthy volunteer with nine deep brain structures manually segmented. The quality of the atlas image allowed for the segmentation of the deep brain structures, which is not possible from the clinical MR head scans of patients. The subject image is non-rigidly registered to the atlas image using thin plate splines to represent the transformation and normalized mutual information as a similarity measure. The obtained transformation is used to map the segmented structures from the atlas to the subject image. We tested the approach on five subjects. The quality of the atlas-based segmentation was evaluated by visual inspection of the third and lateral ventricles, putamena, and caudate nuclei, which are visible in the subject MR images. The agreement of these structures for the five tested subjects was approximately 1 to 2 mm.
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Muhammad Faisal Khan, Muhammad Faisal Khan, Klaus Mewes, Klaus Mewes, Robert E. Gross, Robert E. Gross, Oskar Škrinjar, Oskar Škrinjar, } "Atlas-based segmentation of deep brain structures using non-rigid registration", Proc. SPIE 6918, Medical Imaging 2008: Visualization, Image-Guided Procedures, and Modeling, 69182O (17 March 2008); doi: 10.1117/12.772861; https://doi.org/10.1117/12.772861

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