Objective Evaluation of Accuracy of Intra-Operative Neuroimage Registration
Pre-operative brain images that are registered onto relevant intra-operative images can enhance navigation during image-guided neurosurgery. One of the crucial steps in the process of image registration is assessment of its accuracy. The accuracy of an im
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Abstract Pre-operative brain images that are registered onto relevant intra-operative images can enhance navigation during image-guided neurosurgery. One of the crucial steps in the process of image registration is assessment of its accuracy. The accuracy of an image registration procedure was evaluated in one of our previous studies, for five cases of neurosurgery, using a manual segmentationbased method that is subjective and prone to human errors. The aim of this study is to develop an evaluation method that is objective and automatic. An edgebased Hausdorff Distance (HD) metric based on Canny edges was developed for evaluation. Subsequently, the accuracy of non-rigid registration (NRR) results was evaluated using intra-operative images as ground truth and compared with those
R.R. Garlapati • G.R. Joldes • A. Wittek • J. Lam Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia e-mail: [email protected]; [email protected]; [email protected]; [email protected] N. Weisenfeld • A. Hans • S.K. Warfield Computational Radiology Lab, Children’s Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected]; [email protected]; [email protected] R. Kikinis Surgical Planning Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA e-mail: [email protected] K. Miller () Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia Institute of Mechanics and Advanced Materials, Cardiff School of Engineering, Cardiff University, Wales, UK e-mail: [email protected] A. Wittek et al. (eds.), Computational Biomechanics for Medicine: Models, Algorithms and Implementation, DOI 10.1007/978-1-4614-6351-1 9, © Springer Science+Business Media New York 2013
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from the previous study. The obtained results compared well despite the differences in the methods employed. The edge-based HD metric provides an objective measure for image registration accuracy evaluation.
1 Introduction Neurosurgical resection is the primary therapeutic intervention in the treatment of cerebral gliomas [1]. Near-total surgical removal with the goal of minimising the number of infiltrating glioma cells in the adjacent brain tissue is desirable for several reasons: it prolongs patient’s survival, increases time to malignant progression [2] and decreases risk of seizures [3]. Therefore, the knowledge of white matter tract locations and their relationship to the glioma is as important as defining the tumour’s relationship with eloquent cortex, where the functional significance is clustered. Moreover, damage in certain areas will cause permanent neurological deficits. For example, the functions of primary motor, somatosensory and visual cortex could get affected. Maximising surgical removal of gliomatous tissue, while minimising neurological deficits, is challenging because functional brain tissue may reside close to or even wit
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