The redistributive effect of the public health system: the case of Sierra Leone.

Universal health coverage (UHC), health equity and reduction of income inequalities are key objectives for the Sierra Leone government. While investing in health systems may drive economic growth, it is less clear whether investing in health systems reduces income inequality. Therefore, a crucial issue is to what extent the Sierra Leone public healthcare system reduces income inequality, and finances and provides healthcare services equitably.

Validation of SART 3.5D algorithm for cerebrovascular dynamics and artery versus vein classification in presurgical 3D digital subtraction angiographies.

Classification of arteries and veins in cerebral angiograms can increase the safety of neurosurgical procedures, such as StereoElectroEncephaloGraphy, and aid the diagnosis of vascular pathologies, as arterovenous malformations. We propose a new method for vessel classification using the contrast medium dynamics in rotational digital subtraction angiography (DSA). After 3D DSA and angiogram segmentation, contrast enhanced projections are processed to suppress soft tissue and bone structures attenuation effect and further enhance the CM flow.

Surgical planning assistance in keyhole and percutaneous surgery: A systematic review.

Surgical planning of percutaneous interventions has a crucial role to guarantee the success of minimally invasive surgeries. In the last decades, many methods have been proposed to reduce clinician work load related to the planning phase and to augment the information used in the definition of the optimal trajectory. In this survey, we include 113 articles related to computer assisted planning (CAP) methods and validations obtained from a systematic search on three databases.

Brain-vascular segmentation for SEEG planning via a 3D fully-convolutional neural network.

Three dimensional visualization of vascular structures can assist clinicians in preoperative planning, intra-operative guidance, and post-operative decision-making. The goal of this work is to provide an automatic, accurate and fast method for brain vessels segmentation in Contrast Enhanced Cone Beam Computed Tomography (CE-CBCT) dataset based on a residual Fully Convolutional Neural Network (FCNN). The proposed NN embeds in an encoder-decoder architecture residual elements which decreases the vanishing effect due to deep architecture while accelerating the convergence.

Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics.

Background: Blood vessel segmentation is a topic of high interest in medical image analysis since the analysis of vessels is crucial for diagnosis, treatment planning and execution, and evaluation of clinical outcomes in different fields, including laryngology, neurosurgery and ophthalmology. Automatic or semi-automatic vessel segmentation can support clinicians in performing these tasks. Different medical imaging techniques are currently used in clinical practice and an appropriate choice of the segmentation algorithm is mandatory to deal with the adopted imaging technique characteristics (e.