Automated connectivity-based groupwise cortical atlas generation: Application to data of neurosurgical patients with brain tumors for cortical parcellation prediction

• Published in: Proceedings (International Symposium on Biomedical Imaging) pp. 774 - 777
• Main Authors: Fan Zhang, Kahali, Pegah, Suter, Yannick, Norton, Isaiah, Rigolo, Laura, Savadjiev, Peter, Yang Song, Rathi, Yogesh, Weidong Cai, Wells, William M., Golby, Alexandra J., O'Donnell, Lauren J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.04.2017
• Subjects: Cortical parcellation prediction; embedding atlas; healthy control; Image color analysis; Magnetic resonance imaging; Neurosurgery; neurosurgical patient; Predictive models; Sociology; Statistics; Tumors
• ISSN: 1945-8452
• DOI: 10.1109/ISBI.2017.7950633

This work presents an initial exploration of joint cortical surface and diffusion MRI analysis for neurosurgical patient data. We propose a groupwise cortical modeling strategy that performs an embedding of cortical points from a healthy population and a method for transferring the embedding (with associated information of anatomical label) to patient datasets for cortical parcellation prediction. Our proposed method correlates cortical surfaces based on groupwise white matter connectivity characteristics via a fiber clustering scheme. Unlike other parcellation methods, correspondence of cortical surface vertices is not required. Thus the proposed method can be applied to datasets of patients with brain tumors, using an approximate cortical surface such as a white matter/gray matter boundary derived from diffusion anisotropy. Our initial results on patient data showed good overlap of functional ground truth (subject-specific functional MRI activation areas) with predicted cortical parcels, with 10 of 13 activations overlapping an anatomically corresponding prediction.