Tissue Classification to Support Local Active Delineation of Brain Tumors

In this paper, we demonstrate how a semi-automatic algorithm we proposed in previous work may be integrated into a protocol which becomes fully automatic for the detection of brain metastases. Such a protocol combines 11C-labeled Methionine PET acquisition with our previous segmentation approach. We show that our algorithm responds especially well to this modality thereby upgrading its status from semi-automatic to fully automatic for the presented application. In this approach, the active contour method is based on the minimization of an energy functional which integrates the information provided by a machine learning algorithm. The rationale behind such a coupling is to introduce in the segmentation the physician knowledge through a component capable of influencing the final outcome toward what would be the segmentation performed by a human operator. In particular, we compare the performance of three different classifiers: Naïve Bayes classification, K-Nearest Neighbor classification, and Discriminant Analysis. A database comprising seventeen patients with brain metastases is considered to assess the performance of the proposed method in the clinical environment.

Regardless of the classifier used, automatically delineated lesions show high agreement with the gold standard (R² = 0.98). Experimental results show that the proposed protocol is accurate and meets the physician requirements for radiotherapy treatment purpose.