Bladder segmentation in MRI images using active region growing model.

International audienceProstate segmentation in MRI may be difficult at the interface with the bladder where the contrast is poor. Coupled-models that segment simultaneously both organs with non-overlapping constraints offer a good solution. As a pre-segmentation of the structures of interest is required, we propose in this paper a fast deformable model to segment the bladder. The combination of inflation and internal forces, locally adapted according to the gray levels, allow to deform the mesh toward the boundaries while overcoming the leakage issues that can occur at weak edges. The algorithm, evaluated on 33 MRI volumes from 5 different devices, has shown good performance providing a smooth and accurate surface

Urinary bladder segmentation in CT urography using deepâ learning convolutional neural network and level sets

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/1/mp4498.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134923/2/mp4498_am.pd

Deeply-Supervised CNN for Prostate Segmentation

Prostate segmentation from Magnetic Resonance (MR) images plays an important role in image guided interven- tion. However, the lack of clear boundary specifically at the apex and base, and huge variation of shape and texture between the images from different patients make the task very challenging. To overcome these problems, in this paper, we propose a deeply supervised convolutional neural network (CNN) utilizing the convolutional information to accurately segment the prostate from MR images. The proposed model can effectively detect the prostate region with additional deeply supervised layers compared with other approaches. Since some information will be abandoned after convolution, it is necessary to pass the features extracted from early stages to later stages. The experimental results show that significant segmentation accuracy improvement has been achieved by our proposed method compared to other reported approaches.Comment: Due to a crucial sign error in equation

Deep Convolutional Level Set Method for Image Segmentation

Level Set Method is a popular method for image segmentation. One of the problems in Level Set Method is finding the right initial surface parameter, which implicitly affects the curve evolution and ultimately the segmentation result. By setting the initial curve too far away from the target object, Level Set Method could potentially miss the target altogether, whereas by setting the initial curve as general as possible "“ i.e. capturing the whole image "“ makes Level Set Method susceptible to noise. Recently, deep-learning methods, especially Convolutional Neural Network (CNN), have been proven to achieve state-of-the-art performance in many computer vision tasks such as image classification and detection. In this paper, a new method is proposed, called Deep Convolutional Level Set Method (DCLSM). The idea is to use the CNN object detector as a prior for Level Set Method segmentation. Using DCLSM it is possible to significantly improve the segmentation accuracy and precision of the classic Level Set Method. It was also found that the prior used in the proposed method is the lower and upper bound for DCLSM's precision and recall, respectively

An active contour model for medical image segmentation with application to brain CT image

Cerebrospinal fluid (CSF) segmentation in computed tomography (CT) is a key step in computer-aided detection (CAD) of acute ischemic stroke. Because of image noise, low contrast and intensity inhomogeneity, CSF segmentation has been a challenging task. A region-based active contour model, which is insensitive to contour initialization and robust to intensity inhomogeneity, was developed for segmenting CSF in brain CT images

Characterising the quality of clinical guidelines, epidemiology and healthcare resource utilisation of neurogenic bladder

Background and Aims: Knowledge on several key aspects of the neurogenic bladder (NGB) patient journey remain unknown. Accordingly, the aim of this research was to conduct an in-depth analysis of the prominent NGB clinical guidelines (CGs) and characterise the descriptive epidemiology and healthcare resource utilisation (HRU) of NGB in the UK. Methods: (1) The AGREE II tool was used to appraise the quality of the National Institute for Health and Care Excellence (NICE), European Association of Urology (EAU) and International Consultation on Incontinence (ICI) CGs for NGB and the concordance of their recommendations were assessed. (2) Adults (≥19 years) with a definitive or probable diagnosis of NGB between 1st January 2004 and 31st December 2016 were included into a study using the Clinical Practice Research Datalink (CPRD) GOLD and Hospital Episode Statistics (HES) databases in order to determine their real-world patient characteristics and drug utilisation patterns. Furthermore, the level of HRU over 12 months and associated costs were calculated via a bottom-up approach (ISAC protocol number 17_207RMn). Results: NICE scored 92%, the EAU scored 83% and the ICI scored 75% in the AGREE II appraisal. The CGs place differing emphasis on costs and expert opinion, which translated in notably different recommendations. Amongst many important findings, the CPRD study revealed evidence of diagnosis error in NGB, a high level of comorbidities 8.6 (SD,7.6), polypharmacy 5.2 (SD,4.8), an Anticholinergic Cognitive Burden (ACB) score of 6.6 (SD,5.9), and substantial HRU (overall costs £2,395 per annum). Conclusions: Improving the applicability and incorporation of comparative effectiveness research (CER) is crucial to ensure uptake of CGs and efficiency in clinical practice. It is also imperative that the underlying evidence base is strengthened, and cross-speciality interactions enhanced in order to guide more robust and consistent recommendations in future publications. Furthermore, policy makers should be aware of the substantial burden of complications, polypharmacy, comorbidity, anticholinergic burden and HRU associated with NGB, and modifications to CGs should be introduced to aid in optimal management of these issues