Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration

Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors

Design and synthesis of new molecules based on indolium derivatives for two-photon absorption bioimaging applications

Bioimaging is the visualization of biological processes in real time, interfering as less as possible with these and using non-invasive methods. Among others, fluorescence methods have acquired a very important role in these purposes through the nature of the light. Bioimaging pretends to understand how our organism works identifing subcellular organelles, following cellular processes, quantifying ion or biological species and visualising interactions of molecules with their targets in living cells or tissues. In the last decades, two-photon (TP) microscopy is unseating classical one-photon (OP) microscopy due to its advantages, such as the use of lower energy excitation wavelengths or the possibility of focus in depth, among others. Nowadays, it is an interesting target design and develop of new probes for TP microscopy to biomaging. Fluorophores based on indolenines are a family of compounds with promising properties in this sense. In this work, we present the design, synthesis and characterization of new indolium derivatives with promising properties to be used in bioimaging applications in living cells with different purposes.Real Sociedad Española de Química, Jóvenes Invetigadores Químicos, Facultad de Ciencias Ambientales y Bioquímica (UCLM), Merck, Cortes de Castilla la Mancha, Reaxys (Elsevier), Diputación de Toledo, MestreLab Research, Sección Territorial de Castilla la Mancha (RSEQ), Grupo especializado en Nanociencia y Materiales Moleculares (RESEQ), Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

TICAL - a web-tool for multivariate image clustering and data topology preserving visualization

In life science research bioimaging is often used to study two kinds of features in a sample simultaneously: morphology and co-location of molecular components. While bioimaging technology is rapidly proposing and improving new multidimensional imaging platforms, bioimage informatics has to keep pace in order to develop algorithmic approaches to support biology experts in the complex task of data analysis. One particular problem is the availability and applicability of sophisticated image analysis algorithms via the web so different users can apply the same algorithms to their data (sometimes even to the same data to get the same results) and independently from her/his whereabouts and from the technical features of her/his computer. In this paper we describe TICAL, a visual data mining approach to multivariate microscopy analysis which can be applied fully through the web.We describe the algorithmic approach, the software concept and present results obtained for different example images

Background suppressing Gabor energy filtering

In the field of facial emotion recognition, early research advanced with the use of Gabor filters. However, these filters lack generalization and result in undesirably large feature vector size. In recent work, more attention has been given to other local appearance features. Two desired characteristics in a facial appearance feature are generalization capability, and the compactness of representation. In this paper, we propose a novel texture feature inspired by Gabor energy filters, called background suppressing Gabor energy filtering. The feature has a generalization component that removes background texture. It has a reduced feature vector size due to maximal representation and soft orientation histograms, and it is awhite box representation. We demonstrate improved performance on the non-trivial Audio/Visual Emotion Challenge 2012 grand-challenge dataset by a factor of 7.17 over the Gabor filter on the development set. We also demonstrate applicability of our approach beyond facial emotion recognition which yields improved classification rate over the Gabor filter for four bioimaging datasets by an average of 8.22%

Functionalisation of colloidal transition metal sulphides nanocrystals: A fascinating and challenging playground for the chemist

Metal sulphides, and in particular transition metal sulphide colloids, are a broad, versatile and exciting class of inorganic compounds which deserve growing interest and attention ascribable to the functional properties that many of them display. With respect to their oxide homologues, however, they are characterised by noticeably different chemical, structural and hence functional features. Their potential applications span several fields, and in many of the foreseen applications (e.g., in bioimaging and related fields), the achievement of stable colloidal suspensions of metal sulphides is highly desirable or either an unavoidable requirement to be met. To this aim, robust functionalisation strategies should be devised, which however are, with respect to metal or metal oxides colloids, much more challenging. This has to be ascribed, inter alia, also to the still limited knowledge of the sulphides surface chemistry, particularly when comparing it to the better established, though multifaceted, oxide surface chemistry. A ground-breaking endeavour in this field is hence the detailed understanding of the nature of the complex surface chemistry of transition metal sulphides, which ideally requires an integrated experimental and modelling approach. In this review, an overview of the state-of-the-art on the existing examples of functionalisation of transition metal sulphides is provided, also by focusing on selected case studies, exemplifying the manifold nature of this class of binary inorganic compounds

The influence of laser excitation wavelength on the heating of the neodymium doped fluoride nanoparticles as the result of the multiphonon relaxation

http://www.ester.ee/record=b448534

Microstructural Characterization of Silver Nanoparticles for Bioimaging Applications

Junta de Andalucía FQM-6615, PI0070, FQM31

Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification

Microscopic histology image analysis is a cornerstone in early detection of breast cancer. However these images are very large and manual analysis is error prone and very time consuming. Thus automating this process is in high demand. We proposed a hierarchical system of convolutional neural networks (CNN) that classifies automatically patches of these images into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma. We evaluated our system on the BACH challenge dataset of image-wise classification and a small dataset that we used to extend it. Using a train/test split of 75%/25%, we achieved an accuracy rate of 0.99 on the test split for the BACH dataset and 0.96 on that of the extension. On the test of the BACH challenge, we've reached an accuracy of 0.81 which rank us to the 8th out of 51 teams