Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

Davide Scorza; Sara Moccia; Giuseppe De Luca; Lisa Plaino; Francesco Cardinale; Leonardo S. Mattos; Luis Kabongo; Elena De Momi

doi:10.1117/12.2254474

3 March 2017 Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation

Davide Scorza, Sara Moccia, Giuseppe De Luca, Lisa Plaino, Francesco Cardinale, Leonardo S. Mattos, Luis Kabongo, Elena De Momi

Author Affiliations +

Proceedings Volume 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling; 101352C (2017) https://doi.org/10.1117/12.2254474
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

Stereo-ElectroEncephaloGraphy (SEEG) is a surgical procedure that allows brain exploration of patients affected by focal epilepsy by placing intra-cerebral multi-lead electrodes. The electrode trajectory planning is challenging and time consuming. Various constraints have to be taken into account simultaneously, such as absence of vessels at the electrode Entry Point (EP), where bleeding is more likely to occur. In this paper, we propose a novel framework to help clinicians in defining a safe trajectory and focus our attention on EP. For each electrode, a Maximum Intensity Projection (MIP) image was obtained from Computer Tomography Angiography (CTA) slices of the brain first centimeter measured along the electrode trajectory. A Gaussian Mixture Model (GMM), modified to include neighborhood prior through Markov Random Fields (GMM-MRF), is used to robustly segment vessels and deal with the noisy nature of MIP images. Results are compared with simple GMM and manual global Thresholding (Th) by computing sensitivity, specificity, accuracy and Dice similarity index against manual segmentation performed under the supervision of an expert surgeon. In this work we present a novel framework which can be easily integrated into manual and automatic planner to help surgeon during the planning phase. GMM-MRF qualitatively showed better performance over GMM in reproducing the connected nature of brain vessels also in presence of noise and image intensity drops typical of MIP images. With respect Th, it is a completely automatic method and it is not influenced by inter-subject variability.

Citation Download Citation

Davide Scorza, Sara Moccia, Giuseppe De Luca, Lisa Plaino, Francesco Cardinale, Leonardo S. Mattos, Luis Kabongo, and Elena De Momi "Safe electrode trajectory planning in SEEG via MIP-based vessel segmentation", Proc. SPIE 10135, Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, 101352C (3 March 2017); https://doi.org/10.1117/12.2254474

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