International audienceIn the context of the female pelvic medicine, non-invasive Magnetic Resonance Imaging (MRI) is widely used for the diagnosis of pelvic floor disorders. Nowadays in the clinical routine, diagnoses rely largely on human interpretation of medical images, on the experience of physicians, with sometimes subjective interpretations. Hence, image correlation methods would be an alternative way to assist physicians to provide more objective analyses with standard procedures and parametrization for patient-specific cases. Moreover, the main symptoms of pelvic system pathologies are abnormal mobilities. The FEM (Finite Element Model) simulation is a powerful tool for understanding such mobilities. Both the patient-specific simulation and the image analysis require accurate and smooth geometries of the pelvic organs. This paper introduces a new method that can be classified as a model-to-image correlation approach. The method performs fast semi-automatic detection of the bladder, vagina and rectum from MR images for geometries reconstruction and further study of the mobilities. The approach consists of fitting a B-spline model to the organ shapes in real images via a generated virtual image. We provided efficient, adaptive and consistent segmentation on a dataset of 19 patient images (healthy and pathological)

Female pelvic disorders have a large social impact; the diagnosis of which relies on a key indication: pelvic mobility. The normal mobility is present in a healthy patient, meanwhile the hypermobility can be a sign of female pelvic prolapse and the hypomobility for endometriosis. The evaluation of pelvic mobility is based on medical image analysis. However, the latter does not provide precise values of these indicators directly. Moreover, suspension devices play an important role in pelvic organ function but can hardly be observed on medical images. Our objective is to propose an image-based analysis tool for the quantitative evaluation of pelvic mobility and the shear strain which has an impact on suspension devices. Hence, this paper introduces a such tool based on an efficient and semiautomatic motion tracking of multiple pelvic organs: the bladder, vagina, and rectum presented in dynamic magnetic resonance imaging sequences. The method was validated on prototypical images and applied to different mobility cases. The computed displacement and shear strain fields provide important information on the quality of suspension devices between organs for a fine diagnosis in the clinical context, for example, the early diagnosis of female pelvic prolapse and the localization of possible lesion areas before surgery. Meanwhile, the predicted mobility can be used to compare with the finite element model for numerical simulation.