AbstractsOther

Radiotherapy is a cancer treatment modality that can be considered as a ballistic problem where the tumour must be irradiated while sparing the surrounding healthy organs. Before the beginning of the treatment, the radiation oncologist draws the contours of the organs at risk, tumour and nodal areas on an image of the patient. The accuracy of these contours is crucial to deliver an optimal treatment. In practice, manual drawing is a quite lengthy and repetitive operation, which is moreover subject to certain variability. This thesis aims at providing new tools to facilitate the delineation of organs at risk. For this purpose, an automatic method of organ segmentation is proposed. By automating the most repetitive part of organ drawing, the radiation oncologist can concentrate on the more essential steps of treatment planning. We propose a method that relies on machine learning where the parameters of the method are learnt from already delineated images. The images are first over-segmented in order to determine homogeneous areas and to avoid processing each pixel independently. To determine the organ each area belongs to, we propose to work in an incremental way. The organs are identified one after the other thanks to classification techniques. Organs, which are already identified, contribute to identifying the next ones. We show that this incremental method is effective and allows improving the organ classification accuracy. However a adequate sequence of identification is necessary in order to get optimal results. Two techniques are presented in this work to determine such a sequence of organ classification. These techniques lead to high performances of identification. We also show that a human operator can easily and quickly correct errors made by the incremental approach. Finally, our method being generic, it can be adapted to all regions of the human body. (FSA - Sciences de l)  – UCL, 2014