Simulating Patho-realistic Ultrasound Images using Deep Generative Networks with Adversarial Learning

Ultrasound imaging makes use of backscattering of waves during their interaction with scatterers present in biological tissues. Simulation of synthetic ultrasound images is a challenging problem on account of inability to accurately model various factors of which some include intra-/inter scanline interference, transducer to surface coupling, artifacts on transducer elements, inhomogeneous shadowing and nonlinear attenuation. Current approaches typically solve wave space equations making them computationally expensive and slow to operate. We propose a generative adversarial network (GAN) inspired approach for fast simulation of patho-realistic ultrasound images. We apply the framework to intravascular ultrasound (IVUS) simulation. A stage 0 simulation performed using pseudo B-mode ultrasound image simulator yields speckle mapping of a digitally defined phantom. The stage I GAN subsequently refines them to preserve tissue specific speckle intensities. The stage II GAN further refines them to generate high resolution images with patho-realistic speckle profiles. We evaluate patho-realism of simulated images with a visual Turing test indicating an equivocal confusion in discriminating simulated from real. We also quantify the shift in tissue specific intensity distributions of the real and simulated images to prove their similarity.Comment: To appear in the Proceedings of the 2018 IEEE International Symposium on Biomedical Imaging (ISBI 2018

Teacher professional development through a physical computing workshop

In recent years there has been a push towards more CS and STEM education in Flanders. These two domains require a set of skills with which teachers are currently often unfamiliar. To enable teachers to acquire these skills, professional development programs should be implemented. In this paper we first present a way of identifying the properties of such a program to allow comparison with other programs. Next, we describe a professional development program in the form of a physical computing workshop

Bringing computer science education to secondary school : a teacher first approach

The Progra-MEER professional development workshop is a one year program organized collaboratively by the computer science departments of three Flemish universities. It aims to improve the computer science knowledge of in service teachers in a physical computing context. Since Flemish schools are starting to implement STEM in their schools, the program links computer science to STEM and project based learning. This paper gives a description of the design and implementation of the program while providing an analysis of its strengths and weaknesses. We show that the program leads to the successful implementation of different physical computing projects. However, it needs to further support the practical project implementations while spending more attention on assessment and context definition. Additionally, the program has to invest more effort in creating a sustainable community of practice so knowledge and experiences can still be shared even after the program has finished

Uzawa’s transformation and optimal control problems with variable rates of time preference

Uzawa (1968) first introduced a simple and appealing method for reducing problems with variable rates of time preference to single-state systems by transforming the time scale from t to Δ, a utility discount factor. This transformation has been used extensively, particularly in models of international trade and finance (e.g., Obstfeld, 1981a, 1981b, 1982, Engel and Kletzer, 1989, and Turnovsky, 1997), where the use of a variable rate of time preference avoids some of the “disturbing implications” drawn from typical open-economy Ramsey models. The purpose of this paper, however, is to show that Uzawa’s transformation is valid only when the underlying system to be analyzed is autonomous. Unfortunately, except for the simplest control problems, this is rarely the case. In particular, systems with nonautonomous transition equations imply that the correspondence between Δ and t is no longer unique, and thus Uzawa’s transformation is not applicable