Michael Insana
Editor in Chief

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Q&A Session with the Editor in Chief

Dana Brooks & Jaume Coll-Font & Seyhmus Guler
Northeastern University, USA

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Lunch Demo Abstract
The Center for Integrative Biomedical Computing (CIBC) software suite provides a flexible integrated set of software tools for solving biomedical computing problems, each intended for sub-problems along a pipeline from image analysis and segmentation (Seg3D) and shape modeling (Shapeworks) to surface and volumetric meshing (Cleaver) to modeling, simulation, and inverse solutions (SCIRun), to interactive 3D visualization (ImageVIS3D). SCIRun also features “toolkits” organized for specific problem domains (forward and inverse electocardiography, and transcranial current and magnetic brain stimulation). All our tools work on the three standard operating systems and are professionally engineered and supported. CIBC is a Biomedical Technology Research Resource supported by the NIGMS/NIH.

Jean-Christophe Olivo Marin & Fabrice de Chaumont
Institut Pasteur, France

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Lunch Demo Abstract
Icy is a free and open-source image analysis software platform. It is a versatile tool that can be used both on desktop and on cluster and cloud. The desktop version is for final users requiring a very rich and interactive dedicated client, offering a complete suite of more than 100 build-in core functiond plus 300 contributed online plugins that extend the software functionalities seamlessly. The cluster and cloud client is based on a headless framework dedicated to deployment and schedulers calls, coupled with a new Icy-made streaming library for heavy data access, either stored locally or remotely.
Icy takes advantage of a centralized repository, that collects and distributes plugins, scripts and graphical protocols, organizes user’s support, displays plugin usage statistics, and as the community requested, allows the rating of the plugins by the users. Icy has accrued a base of more than one thousand regular users. Statistics on usage, community and other aspects are refreshed daily and available on this
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Lee Kamentsky
Broad Institute of Harvard and MIT, USA

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Lunch Demo Abstract
CellProfiler is free open-source software designed to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. See our papers on and .

Alexandre Gramfort & Bertrand Thirion
Telecom ParisTech & INRIA, France

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Lunch Demo Abstract
Machine Learning is the branch of computer science concerned with the development of algorithms which can learn from previously-seen data in order to make predictions about future data, and has become an important part of research in many scientific fields. This demo will introduce the basics of machine learning, and how these learning tasks can be accomplished using Scikit-Learn, a machine learning library written in Python and built on NumPy, SciPy, and Matplotlib. The demo will be illustrated with functional brain imaging data.

Guang-Zhong Yang & May D. Wang
Editor in Chief & Senior Editor of JBHI

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Lunch Demo Abstract
Developments in the fields of biomedical and health informatics are driving major expansion in big-data, not only because of the sheer volume of information generated, but also due to the complexity, diversity, and the rich context of the data that encompasses discoveries from basic sciences to clinical translation, to health systems and large-scale population studies on determinants of health. The purpose of this lunch session is to discuss the challenges and opportunities of large scale image informatics, discussing new analytic tools to facilitate scalable, accessible and sustainable data infrastructure for effective management of large, multiscale, multimodal, distributed and heterogeneous data sets and convert data into knowledge for support targeted therapy and minimally invasive surgery, drug discovery, disease management, and care delivery.

The IEEE Journal of Biomedical and Health Informatics (J-BHI), which is one of the leading journals in computer science and information systems with a strong interdisciplinary focus and biomedical and health application emphasis. It publishes original papers describing recent advances in the field of biomedical and health informatics where information and communication technologies intersect with health, healthcare, life sciences and biomedicine.

Uran Ferizi
University College London, UK

Benoit Scherrer
Boston Children’s Hospital, Harvard Medical School, USA

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Challenge Abstract
The last decade has seen an emergence of many new diffusion MRI models to describe diffusion signal in neural tissue. However, competing models are rarely tested and compared on a common dataset. Furthermore, it is unclear how much the performance of a particular model depends on a specific set of diffusion encoding parameters and how well it generalizes to unseen data. Finding an accurate model that explains a large range of different diffusion MRI measurements is important, because it helps us identify the sensitivity of our models to capture the underlying tissue microstructure.

This challenge poses to find the best model in two different tissue configurations that are common in the brain: 1) fibers are approximately straight and parallel 2) more complex fiber configurations, such as fiber crossing, bending or fanning. For both configurations, we provide a broad set of measurements that covers the set of b-values and diffusion times as widely as possible. Challenge participants have access to three-quarters of the whole dataset; the winning model is the one that predicts the remaining ‘unseen’ quarter most closely.

Sparse modeling and the resolution of inverse problems in biomedical imaging

Summary

Sparsity is a powerful paradigm for introducing prior constraints on signals in order to address ill-posed image reconstruction problems.
In this talk, we first present a continuous-domain statistical framework that supports the paradigm. We consider stochastic processes that are solutions of non-Gaussian stochastic differential equations driven by white Lévy noise. We show that this yields intrinsically sparse signals in the sense that they admit a concise representation in a matched wavelet basis.
We apply our formalism to the discretization of ill-conditioned linear inverse problems where both the statistical and physical measurement models are projected onto a linear reconstruction space. This leads to the specification of a general class of maximum a posteriori (MAP) signal estimators complemented with a practical iterative reconstruction scheme. While our family of estimators includes the traditional methods of Tikhonov and total-variation (TV) regularization as particular cases, it opens the door to a much broader class of potential functions that are inherently sparse and typically nonconvex. We apply our framework to the reconstruction of images in a variety of modalities including MRI, phase-contrast tomography, cryo-electron tomography, and deconvolution microscopy.
Finally, we investigate the possibility of specifying signal estimators that are optimal in the MSE sense. There, we consider the simpler denoising problem and present a direct solution for first-order processes based on message passing that serves as our gold standard. We also point out some of the pitfalls of the MAP paradigm (in the non-Gaussian setting) and indicate future directions of research.
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Biography

Michael Unser is professor and director of EPFL’s Biomedical Imaging Group, Lausanne, Switzerland. His primary area of investigation is biomedical image processing. He is internationally recognized for his research contributions to sampling theory, wavelets, the use of splines for image processing, stochastic processes, and computational bioimaging. He has published over 250 journal papers on those topics. He is the author with P. Tafti of the book “An introduction to sparse stochastic processes”, Cambridge University Press 2014.

From 1985 to 1997, he was with the Biomedical Engineering and Instrumentation Program, National Institutes of Health, Bethesda USA, conducting research on bioimaging.

Dr. Unser has held the position of associate Editor-in-Chief (2003-2005) for the IEEE Transactions on Medical Imaging. He is currently member of the editorial boards of SIAM J. Imaging Sciences, IEEE J. Selected Topics in Signal Processing, and Foundations and Trends in Signal Processing.
He co-organized the first IEEE International Symposium on Biomedical Imaging (ISBI2002) in Washington, DC, and was the founding chair of the technical committee of the IEEE-SP Society on Bio Imaging and Signal Processing (BISP).
Prof. Unser is a fellow of the IEEE (1999), an EURASIP fellow (2009), and a member of the Swiss Academy of Engineering Sciences. He is the recipient of several international prizes including three IEEE-SPS Best Paper Awards and two Technical Achievement Awards from the IEEE (2008 SPS and EMBS 2010).

Navigating the cellular landscape with new optical probes, imaging strategies and technical innovations

Biography

Jennifer Lippincott-Schwartz is Section Chief of the Cell Biology and Metabolism Branch, NICHD, NIH and NIH Distinguished Investigator. She received her BA from Swarthmore College, MS in Biology from Stanford University and PhD in Biochemistry from the Johns Hopkins University. Her research uses live cell imaging approaches to analyze the spatio-temporal behavior and dynamic interactions of molecules and organelles in cells. Her group has pioneered the use of green fluorescent protein (GFP) technology for quantitative analysis and modeling of intracellular protein traffic and organelle biogenesis in live cells and embryos, providing novel insights into cell compartmentalization, protein trafficking and organelle inheritance. Most recently, her research has focused on the development and use of photoactivatable fluorescent proteins, including the development photoactivated localization microscopy, (i.e., PALM), a superresolution imaging technique that enables visualization of molecule distributions at high density at the nano-scale.

Her work has been recognized with election to the National Academy of Sciences and the National Institute of Medicine, and with the Royal Microscopy Society Pearse Prize and the Society of Histochemistry Feulgen Prize. She is President of the American Society of Cell Biology for 2014. She serves on the scientific advisory boards of the Howard Hughes Medical Institute, the Weizmann Institute of Sciences, the Searle Scholar Program, and the Salk Institute.