Prediagnosis of pelvic floor disordersbased image registration and clustering Pelvic dysfunction mainly affects adult women, it is estimated that 15% of multiparous women suffer from the problem. Dysfunctions can be diagnosed by defecography, a dynamic MRI scan. Images are used by specialists to diagnose organ dysfunction such as the bladder and the early rectum. This paper presents an automated classification system that uses a nonrigid registration based on a variational model to create automatic markings from initial markings made by an expert. The classification is based on simple average and the centroids of the Kmeans grouping technique. The classification made by the system is evaluated by confusion matrix based metrics. The obtained results using 21 defecography exams from 21 different patients indicate that the proposed technique is a promising tool in the diagnosis of pelvic floor disorders and can assist the physician in the diagnostic process.

A nonlocal based segmentation method for Pelvic MR Images A number of people are infected with functional disorders of the pelvic floor such as pelvic organ prolapse and defecation dysfunction. For radiologists, to discern anatomical structures from tomographic images is an important way to find such diseases. However, it is a challenge to recognize the anatomical structures from pelvic MR (magnetic resonance) images, since the same pelvic organs usually are with various shapes and different organs sometimes are with similar appearance in MR images. To address this problem, we introduce a nonlocal module in the deep neural networks so that both the local features and the global features could be fused to segment organs. Moreover, for the very small quantities of the open pelvic MR images, we have built up a finegrained MR image segmentation dataset annotated by professional medical researchers, and proposed a data augmentation strategy that is especially suitable for our dataset. In the primary experiments on the dataset, the proposed method in this paper is compared with stateoftheart image segmentation methods and achieves better performance.