A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Conformal Field Theories, Graphs and Quantum Algebras

This article reviews some recent progress in our understanding of the structure of Rational Conformal Field Theories, based on ideas that originate for a large part in the work of A. Ocneanu. The consistency conditions that generalize modular invariance for a given RCFT in the presence of various types of boundary conditions --open, twisted-- are encoded in a system of integer multiplicities that form matrix representations of fusion-like algebras. These multiplicities are also the combinatorial data that enable one to construct an abstract ``quantum'' algebra, whose $6j$- and $3j$-symbols contain essential information on the Operator Product Algebra of the RCFT and are part of a cell system, subject to pentagonal identities. It looks quite plausible that the classification of a wide class of RCFT amounts to a classification of ``Weak $C^*$- Hopf algebras''.Comment: 23 pages, 12 figures, LateX. To appear in MATHPHYS ODYSSEY 2001 --Integrable Models and Beyond, ed. M. Kashiwara and T. Miwa, Progress in Math., Birkhauser. References and comments adde

Passage of radiation through wormholes

We investigate numerically the process of the passage of a radiation pulse through a wormhole and the subsequent evolution of the wormhole that is caused by the gravitational action of this pulse. The initial static wormhole is modeled by the spherically symmetrical Armendariz-Picon solution with zero mass. The radiation pulses are modeled by spherically symmetrical shells of self-gravitating massless scalar fields. We demonstrate that the compact signal propagates through the wormhole and investigate the dynamics of the fields in this process for both cases: collapse of the wormhole into the black hole and for the expanding wormhole.Comment: 18 Pages, 13 figures, minor typos corrected, updated reference

Modelling Neuron Morphology: Automated Reconstruction from Microscopy Images

Understanding how the brain works is, beyond a shadow of doubt, one of the greatest challenges for modern science. Achieving a deep knowledge about the structure, function and development of the nervous system at the molecular, cellular and network levels is crucial in this attempt, as processes at all these scales are intrinsically linked with higher-order cognitive functions. The research in the various areas of neuroscience deals with advanced imaging techniques, collecting an increasing amounts of heterogeneous and complex data at different scales. Then, computational tools and neuroinformatics solutions are required in order to integrate and analyze the massive quantity of acquired information. Within this context, the development of automaticmethods and tools for the study of neuronal anatomy has a central role. The morphological properties of the soma and of the axonal and dendritic arborizations constitute a key discriminant for the neuronal phenotype and play a determinant role in network connectivity. A quantitative analysis allows the study of possible factors influencing neuronal development, the neuropathological abnormalities related to specific syndromes, the relationships between neuronal shape and function, the signal transmission and the network connectivity. Therefore, three-dimensional digital reconstructions of soma, axons and dendrites are indispensable for exploring neural networks. This thesis proposes a novel and completely automatic pipeline for neuron reconstruction with operations ranging from the detection and segmentation of the soma to the dendritic arborization tracing. The pipeline can deal with different datasets and acquisitions both at the network and at the single scale level without any user interventions or manual adjustment. We developed an ad hoc approach for the localization and segmentation of neuron bodies. Then, various methods and research lines have been investigated for the reconstruction of the whole dendritic arborization of each neuron, which is solved both in 2D and in 3D images

Photon Physics in Heavy Ion Collisions at the LHC

Various pion and photon production mechanisms in high-energy nuclear collisions at RHIC and LHC are discussed. Comparison with RHIC data is done whenever possible. The prospect of using electromagnetic probes to characterize quark-gluon plasma formation is assessed.Comment: Writeup of the working group "Photon Physics" for the CERN Yellow Report on "Hard Probes in Heavy Ion Collisions at the LHC", 134 pages. One figure added in chapter 5 (comparison with PHENIX data). Some figures and correponding text corrected in chapter 6 (off-chemical equilibrium thermal photon rates). Some figures modified in chapter 7 (off-chemical equilibrium photon rates) and comparison with PHENIX data adde

A Segmentation Method for fluorescence images without a machine learning approach

Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps, and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing Indirect ImmunoFluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is far from the conventional neural network approach, but it is equivalent to their quantitative and qualitative performance, and it is also solid to adversative noise. The method is robust, based on formally correct functions, and does not suffer from tuning on specific data sets. Results: This work demonstrates the robustness of the method against the variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on two datasets (Neuroblastoma and NucleusSegData) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional to a structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) to segment cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.Comment: 25 page