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

The role of critical current on point contact Andreev Reflection spectrum between a normal metal and a superconductor

The point contact spectrum between a normal metal and a superconductor often shows unexpected sharp dips in the conductance at voltage values larger than the superconducting energy gap. These dips are not predicted in the Blonder-Tinkham-Klapwizk (BTK) theory, commonly used to analyse these contacts. We present here a systematic study of these dips in a variety of contacts between different combinations of a superconductor and a normal metal. From the correlation between the characteristics of these dips with the contact area, we can surmise that such dips are caused by the contact not being in the ballistic limit. An analysis of the possible errors introduced while analysing such a spectrum with the standard BTK model is also presented.Comment: 16 pages, PS, figure include

Comment on "Spectroscopic Evidence for Multiple Order Parameters in the Heavy Fermion Superconductor CeCoIn5"

This is a comment on recent paper by Rourke et al. titled "Spectroscopic Evidence for Multiple Order Parameters in the Heavy Fermion Superconductor CeCoIn5" (cond-mat/0409562v1 22 Sep 2004). We argue that the features observed by Rourke etal. arise from their contact not being in the ballistic limit.Comment: 4 pages with figures (pdf

Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member auto-encoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33\% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 . Comparison with other major algorithms substantiates the high efficacy of our model.Comment: Accepted as a conference paper at IEEE EMBC, 201