Technical aspects and evaluation methodology for the application of two automated brain MRI tumor segmentation methods in radiation therapy planning

Abstract

The purpose of this study was to design the steps necessary to create a tumor volume outline from the results of two automated multispectral magnetic resonance imaging segmentation methods and integrate these contours into radiation therapy treatment planning. Algorithms were developed to create a closed, smooth contour that encompassed the tumor pixels resulting from two automated segmentation methods: k-nearest neighbors and knowledge guided. These included an automatic three-dimensional (3D) expansion of the results to compensate for their undersegmentation and match the extended contouring technique used in practice by radiation oncologists. Each resulting radiation treatment plan generated from the automated segmentation and from the outlining by two radiation oncologists for 11 brain tumor patients was compared against the volume and treatment plan from an expert radiation oncologist who served as the control. As part of this analysis, a quantitative and qualitative evaluation mechanism was developed to aid in this comparison. It was found that the expert physician reference volume was irradiated within the same level of conformity when using the plans generated from the contours of the segmentation methods. In addition, any uncertainty in the identification of the actual gross tumor volume by the segmentation methods, as identified by previous research into this area, had small effects when used to generate 3D radiation therapy treatment planning due to the averaging process in the generation of margins used in defining a planning target volume.