An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark

Ultrasound Guidance in Paravertebral Injections of Oxygen-Ozone: Treatment of Low Back Pain

Background: Paravertebral injection of ozone is an established clinical practice for the treatment of Low Back Pain (LBP). The role of Ultrasound Guidance (USG) in mini invasive procedures has become important in many clinical practice thanks to the greater precision this technique can add. As matter of fact, a large volume of ozone in a single administration may have some adverse or side effects. In this study we wanted to verify if the use of USG in Oxygen/ Ozone (O2/O3) infiltrations could allow the administration of a smaller volume of gas mixture, increasing the safety and the comfort of the procedure itself, obtaining however similar or better results in pain decrease. Methods: We compared two groups of 25 patients affected by LBP, undergoing 10 infiltrations of O2/O3, by using USG (group U) or only anatomical landmarks (group AL). Pain intensity, by calculating Visual Analogical Scale (VAS) difference before and after the treatment, and the discomfort were evaluated in both groups. Results: The mean of the VAS before the treatment was 6.44 in group U and 6.48 in group AL. The mean of the VAS after the treatment was 2.22 in group U and 3.04 in group AL. The mean of discomfort rate was 2.84 in group U and 5.44 in group AL. The number of patients with unbearable discomfort was 0 in group U and 7 in group AL. Conclusions: As many other treatment, also paravertebral injections of O2/O3 benefits of the advantages of the US device which makes this treatment safer and more accurate

The Athena Astrophysical MHD Code in Cylindrical Geometry

A method for implementing cylindrical coordinates in the Athena magnetohydrodynamics (MHD) code is described. The extension follows the approach of Athena's original developers and has been designed to alter the existing Cartesian-coordinates code as minimally and transparently as possible. The numerical equations in cylindrical coordinates are formulated to maintain consistency with constrained transport, a central feature of the Athena algorithm, while making use of previously implemented code modules such as the Riemann solvers. Angular-momentum transport, which is critical in astrophysical disk systems dominated by rotation, is treated carefully. We describe modifications for cylindrical coordinates of the higher-order spatial reconstruction and characteristic evolution steps as well as the finite-volume and constrained transport updates. Finally, we present a test suite of standard and novel problems in one-, two-, and three-dimensions designed to validate our algorithms and implementation and to be of use to other code developers. The code is suitable for use in a wide variety of astrophysical applications and is freely available for download on the web