Approche hybride combinant champs de Markov et modèle statistique de forme pour l’extraction des contours de la prostate en IRM

Recent progress in magnetic resonance imaging (MRI) has enabled new prostate cancer diagnosis techniques. The newest challenges in this field are to enhance image-based tumours detection. In such a context, the extraction of prostate's contours is a crucial step in the interpretation of MR images, and is usually carried out by an expert radiologist. This is though a tedious time consuming task, especially in 3D images (like CT and MRI). In addition, manual delineation is not reproducible because of differences between observers. In this paper, we introduce a novel method for automatic segmentation of prostate MRI that could help physicians in extracting 3D outlines of the gland. First a deformable shape model is used to obtain a first segmentation. The latter is refined using intensity information and Markov Random Fields modelling of regions. We use the Iterative Conditional Mode for optimising voxels’ labelling according to a Maximum A Posteriori criterion. Results from evaluation on patients’ data show that the method is satisfyingly accurate, fast and robust which makes it suitable for use in a clinical context. A multicentric validation and transfer to the industry would bring the contributions of this method to clinical routine and help improving diagnosis of prostate cancer.