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Tumor treating fields (TTFields) is an FDA approved therapy for the treatmentof Gliobastoma Multiform (GBM) and currently being investigated for additionaltumor types. TTFields are delivered to the tumor through the placement oftransducer arrays (TAs) placed on the patient scalp. The positions of the TAsare associated with treatment outcomes via simulations of the electric fields.Therefore, we are currently developing a method for recommending optimalplacement of TAs. A key step to achieve this goal is to correctly segment thehead into tissues of similar electrical properties. Visual inspection ofsegmentation quality is invaluable but time-consuming. Automatic qualityassessment can assist in automatic refinement of the segmentation parameters,suggest flaw points to the user and indicate if the segmented method is ofsufficient accuracy for TTFields simulation. As a first step in this direction,we identified a set of features that are relevant to atlas-based segmentationand show that these are significantly correlated (p < 0.05) with a similaritymeasure between validated and automatically computed segmentations.Furthermore, we incorporated these features in a decision tree regressor topredict the similarity of the validated and computed segmentations of 20TTFields patients using a leave-one-out approach. The predicted similaritymeasures were highly correlated with the actual ones (average abs. difference3% (SD = 3%); r = 0.92, p < 0.001). We conclude that quality estimation ofsegmentations is feasible by incorporating machine learning andsegmentation-relevant features.

Evaluation of head segmentation quality for treatment planning of tumor treating fields in brain tumors