Brain segmentation tools under uncertain conditions for radiotherapy treatment planning

Objective : Radiotherapy Treatment Planning (RTP) is a procedure to plan the irradiation treatment which is usually simulated on a conventional simulator before applying it on the patient. The main goal is to deliver the adequate irradiation dose to a tumour without causing severe damage to surrounding normal and health tissues. Material and methods : The major weaknesses of current RTP systems come from their rendering methods since most of them use surface rendering rather than volume rendering. All target objects and other critical organs are required to be modelled with interactive contouring slice by slice. The sizes of the segmentation objects are not accurate and some of small but critical organs sometimes maybe neglected. Results : Image segmentation is currently used into several medical imaging applications that involve diagnosis or treatment. Segmentation of volumes is an essential tool for the radiation therapy treatment of the cancer. One of the key organs that must be protected during the irradiation treatment is the brain. Nowadays, high resolution computed tomography (CT) data are required to perform accurate 3D treatment planning, and therefore there is the demand for quick but at the same time accurate segmentation tools. Inappropriate contours results have been performed for the cases where uncertain conditions are appeared (like position of the body in the table and metal material of the bed). Conclusions : In this work we presented an algorithm that can be used for the accurate semi-automatic segmentation of the brain in three dimensions (3D) from CT images. Our method, which is currently in clinical use, is basically composed from an edge detection algorithm and statistical extreme values techniques (outliers).