Is endoanal, introital or transperineal ultrasound diagnosis of sphincter defects more strongly associated with anal incontinence?

INTRODUCTION AND HYPOTHESIS: Our aim was to explore the association between anal incontinence (AI) and persistent anal sphincter defects diagnosed with 3D endoanal (EAUS), introital (IUS) and transperineal ultrasound (TPUS) in women after obstetric anal sphincter injury (OASI) and study the association between sphincter defects and anal pressure. METHODS: We carried out a cross-sectional study of 250 women with OASI recruited during the period 2013-2015. They were examined 6-12 weeks postpartum or in a subsequent pregnancy with 3D EAUS, IUS and TPUS and measurement of anal pressure. Prevalence of urgency/solid/liquid AI or flatal AI and anal pressure were compared in women with a defect and those with an intact sphincter (diagnosed off-line) using Chi-squared and Mann-Whitney U test. RESULTS: At a mean of 23.6 (SD 30.1) months after OASI, more women with defect than those with intact sphincters on EAUS had AI; urgency/solid/liquid AI vs external defect: 36% vs 13% and flatal AI vs internal defect: 27% vs 13%, p < 0.05. On TPUS, more women with defect sphincters had flatal AI: 32% vs 13%, p = 0.03. No difference was found on IUS. Difference between defect and intact sphincters on EAUS, IUS and TPUS respectively was found for mean [SD] maximum anal resting pressure (48 [13] vs 55 [14] mmHg; 48 [12] vs 56 [13] mmHg; 50 [13] vs 54 [14] mmHg) and squeeze incremental pressure (33 [17] vs 49 [28] mmHg; 37 [23] vs 50 [28] mmHg; 36 [18] vs 50 [30] mmHg; p < 0.01). CONCLUSIONS: Endoanal ultrasound had the strongest association with AI symptoms 2 years after OASI. Sphincter defects detected using all ultrasound methods were associated with lower anal pressure

TPU Cloud-Based Generalized U-Net for Eye Fundus Image Segmentation

Medical images from different clinics are acquired with different instruments and settings. To perform segmentation on these images as a cloud-based service we need to train with multiple datasets to increase the segmentation independency from the source. We also require an ef cient and fast segmentation network. In this work these two problems, which are essential for many practical medical imaging applications, are studied. As a segmentation network, U-Net has been selected. U-Net is a class of deep neural networks which have been shown to be effective for medical image segmentation. Many different U-Net implementations have been proposed.With the recent development of tensor processing units (TPU), the execution times of these algorithms can be drastically reduced. This makes them attractive for cloud services. In this paper, we study, using Google's publicly available colab environment, a generalized fully con gurable Keras U-Net implementation which uses Google TPU processors for training and prediction. As our application problem, we use the segmentation of Optic Disc and Cup, which can be applied to glaucoma detection. To obtain networks with a good performance, independently of the image acquisition source, we combine multiple publicly available datasets (RIM-One V3, DRISHTI and DRIONS). As a result of this study, we have developed a set of functions that allow the implementation of generalized U-Nets adapted to TPU execution and are suitable for cloud-based service implementation.Ministerio de Economía y Competitividad TEC2016-77785-

Automatic analysis of transperineal ultrasound images

This thesis focuses on the automatic image analysis of transperineal ultrasound (TPUS) data, which is used to investigate female pelvic floor problems. These problems have a high prevalence, but the understanding of pelvic floor (dys-)function is limited. TPUS analysis of the pelvic floor is done manually, which is time-consuming and observer dependent. This hinders both the research into interpretation of TPUS data and its clinical use. To overcome these problems we use automatic image analysis. Currently, one of the main methods used, to analyse the TPUS is manually selecting and segmenting the slice of minimal hiatal dimensions (SMHD). In the first chapter of this thesis we show that reliable automatic segmentation of the urogenital hiatus and the puborectalis muscle in the SMHD can be successfully implemented, using deep learning. Furthermore, we show that this can also be used to successfully automate the process of selecting and segmenting the SMHD. 4D TPUS is available in the clinical practice but by the aforementioned method only provides 1D and 2D parameters. Therefore, information stored within TPUS about the volume appearance of the pelvic floor muscles and muscle functionality is not analyzed. In the third chapter of this thesis we propose a reproducible manual 3D segmentation protocol of the puborectalis muscle. The resulting manual segmentations can be used to train active appearance models and convolutional neural networks, these algorithms can be used for reliable automatic 3D segmentation. In the fifth chapter of we show that on this data it is possible to identify all subdivisions of the main pelvic floor muscle group, the levator ani muscles, on new TPUS data. In the last chapter we apply unsupervised deep learning to our data and show that this can be used for classification of the TPUS data. The segmentation results presented in this thesis are an important step to reduce the TPUS analysis time and will therefore ease the study of large populations and clinical TPUS analysis. The 3D identification and segmentation of the levator ani muscle subdivisions helps to identify if they are still intact. This is an important step to better informed clinical decision-making

Role of ultrasound in colorectal diseases

Ultrasound is an undervalued non-invasive examination in the diagnosis of colonic diseases. It has been replaced by the considerably more expensive magnetic resonance imaging and computed tomography, despite the fact that, as first examination, it can usefully supplement the diagnostic process. Transabdominal ultrasound can provide quick information about bowel status and help in the choice of adequate further examinations and treatment. Ultrasonography, as a screening imaging modality in asymptomatic patients can identify several colonic diseases such as diverticulosis, inflammatory bowel disease or cancer. In addition, it is widely available, cheap, non-invasive technique without the use of ionizing radiation, therefore it is safe to use in childhood or during pregnancy, and can be repeated at any time. New ultrasound techniques such as elastography, contrast enhanced and Doppler ultrasound, mini-probes rectal and transperineal ultrasonography have broadened the indication. It gives an overview of the methodology of various ultrasound examinations, presents the morphology of normal bowel wall and the typical changes in different colonic diseases. We will pay particular attention to rectal and transperineal ultrasound because of their outstanding significance in the diagnosis of rectal and perineal disorders. This article seeks to overview the diagnostic impact and correct indications of bowel ultrasound