Tumor Tissue Segmentation for Seed-based Mapping of Peritumoral Resting-State Connectivity in Patients with Glioblastomas

In this study, we developed a model-free seed selection approach using deep learning-based tumor tissue segmentation in combination with iterative subject-specific seed-optimization which improves the specificity of peri-tumoral seed selection. The methodology automates seed placement in the vicinity of the tumor in the zone that is at risk during surgical resection without relying on neurofunctional brain atlases. Evaluation of cortical eloquence in different tumor subregions, such as edematous and infiltrative regions was feasible using a single MRI contrast. This computationally efficient approach was integrated into a real-time resting-state fMRI analysis pipeline to characterize peri-tumoral connectivity in patients with glioblastomas.