NIMG-20. EVALUATION OF HEAD SEGMENTATION QUALITY FOR TREATMENT PLANNING OF TUMOR TREATING FIELDS IN BRAIN TUMORS

摘要

Tumor treating fields (TTFields) is an FDA approved therapy for the treatment of glioblastoma multiform (GBM), malignant pleural mesothelioma (MPM), and currently being investigated for additional tumor types. TTFields are delivered to the tumor through the placement of transducer arrays (TAs) placed on the patient’s shaved scalp. The positions of the TAs are associated with treatment outcomes via simulations of the electric fields. Therefore, we are currently developing a method for recommending optimal placement of TAs. A key step to achieve this goal is to correctly segment the head into tissues of similar electrical properties. Visual inspection of segmentation quality is invaluable but time-consuming. Automatic quality assessment can assist in automatic refinement of the segmentation parameters, suggest flaw points to the user and indicate if the segmented method is of sufficient accuracy for TTFields simulation. As a first step in this direction, we identified a set of features that are relevant to atlas-based segmentation and show that these are significantly correlated (p < 0.05) with a similarity measure between gold-standard and automatically computed segmentations. Furthermore, we incorporated these features in a decision tree regressor to predict the similarity of the gold-standard and computed segmentations of 20 TTFields patients using a leave-one-out approach. The predicted similarity measures were highly correlated with the actual ones (average absolute difference 3% (SD = 3%); r = 0.92, p < 0.001). We conclude that automatic quality estimation of segmentations is feasible by incorporating segmentation-relevant features with statistical and machine learning methods, such as decision tree regressor.