Image informatics strategies for deciphering neuronal network connectivity

Brain function relies on an intricate network of highly dynamic neuronal connections that rewires dramatically under the impulse of various external cues and pathological conditions. Among the neuronal structures that show morphologi- cal plasticity are neurites, synapses, dendritic spines and even nuclei. This structural remodelling is directly connected with functional changes such as intercellular com- munication and the associated calcium-bursting behaviour. In vitro cultured neu- ronal networks are valuable models for studying these morpho-functional changes. Owing to the automation and standardisation of both image acquisition and image analysis, it has become possible to extract statistically relevant readout from such networks. Here, we focus on the current state-of-the-art in image informatics that enables quantitative microscopic interrogation of neuronal networks. We describe the major correlates of neuronal connectivity and present workflows for analysing them. Finally, we provide an outlook on the challenges that remain to be addressed, and discuss how imaging algorithms can be extended beyond in vitro imaging studies

Characterization of structural changes in spinal vertebrae based on perturbations to an adaptive model

Diffuse Idiopathic Skeletal Hyperostosis, or DISH, is a disease characterized by ossification of the entheses and the anterior longitudinal ligament. The diagnosis is made by visual analysis of an X-ray by a professional using the Resnick Criterion. The different experience among professionals and the fact that this criterion is only suitable in advanced stages of the disease make diagnosis difficult. Therefore, this work aims to contribute to the development of an auxiliary diagnostic tool for this disease. For this, a semi-automatic vertebral segmentation algorithm based on active morphological contours was proposed, comparing it with previous work and with segmentations made by experts on two radiographic images. Next, the corners of the vertebrae, where the disease manifests itself, were analyzed in order to characterize images with DISH. To accomplish this, it was assumed symmetry of the vertebrae and a Gaussian distribution of the histograms of those corners to analyze them and calculate two ratios: Left upper corner mean value / Right upper corner mean value (LS/RS) and Left lower corner mean value / Right lower corner mean value (LI/RI), in order to find a differentiating metric between vertebrae with pathology and those without. The results achieved by the algorithm were clearly superior to the previous work and similar to that of the experts. The analysis of pathologic vertebrae revealed a difference in the shift of the distributions of pathologic corners relative to non-pathologic ones, which is not seen in vertebrae without apparent pathology. Regarding the ratios, the LI/RI proved to be particularly effective in differentiating, being closer to 1 when pathology is not present.A Hiperostose Esquelética Idiopática Difusa, ou DISH, é uma doença caracterizada pela ossificação das entéses e do ligamento longitudinal anterior. O diagnóstico é realizado pela análise visual de um raio-X, por um profissional, utilizando o Critério de Resnick. A diferente experiência entre profissionais e o facto de este critério só ser adequado em fases avançadas da doença tornam o diagnóstico difícil. Por isso, este trabalho visa contribuir para o desenvolvimento de um instrumento auxiliar de diagnóstico desta doença. Para isso, foi proposto um algoritmo de segmentação de vertebras, semi-automático, baseado em contornos morfológicos ativos, comparando-o com o trabalho anterior e com as segmentações feitas por especialistas em duas imagens radiográficas. De seguida, foram analisadas as extremidades das vértebras, onde a doença se manifesta, com o objetivo de identificar imagens com DISH. Para tal, assumiu-se a simetria das vértebras e uma distribuição Gaussiana dos histogramas das extremidades para analisar as mesmas e calcular dois rácios: Valor médio do canto superior esquerdo / Valor médio do canto superior direito(LS/RS) e valor médio do canto inferior esquerdo /Valor médio do canto inferior direito(LI/RI), a fim de encontrar uma métrica diferenciadora das vértebras com patologia das não patológicas. Os resultados conseguidos pelo algoritmo foram claramente superiores ao do trabalho anterior e semelhantes ao dos peritos. A análise das vértebras patológicas revelou uma diferença na deslocação das distribuições dos cantos patológicos relativamente aos não patológicos, o que não se verifica em vértebras sem patologia aparente. Relativamente aos rácios, o LI/RI mostrou ser particularmente eficaz na diferenciação, estando mais próximo de 1 quando a patologia não está presente

Morphological feature extraction for statistical learning with applications to solar image data

Abstract: Many areas of science are generating large volumes of digital image data. In order to take full advantage of the high-resolution and high-cadence images modern technology is producing, methods to automatically process and analyze large batches of such images are needed. This involves reducing complex images to simple representations such as binary sketches or numerical summaries that capture embedded scientific information. Using techniques derived from mathematical morphology, we demonstrate how to reduce solar images into simple ‘sketch ’ representations and numerical summaries that can be used for statistical learning. We demonstrate our general techniques on two specific examples: classifying sunspot groups and recognizing coronal loop structures. Our methodology reproduces manual classifications at an overall rate of 90 % on a set of 119 magnetogram and white light images of sunspot groups. We also show that our methodology is competitive with other automated algorithms at producing coronal loop tracings and demonstrate robustness through noise simulations. 2013 Wile

Book of Abstracts 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering and 3rd Conference on Imaging and Visualization

In this edition, the two events will run together as a single conference, highlighting the strong connection with the Taylor & Francis journals: Computer Methods in Biomechanics and Biomedical Engineering (John Middleton and Christopher Jacobs, Eds.) and Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (JoãoManuel R.S. Tavares, Ed.). The conference has become a major international meeting on computational biomechanics, imaging andvisualization. In this edition, the main program includes 212 presentations. In addition, sixteen renowned researchers will give plenary keynotes, addressing current challenges in computational biomechanics and biomedical imaging. In Lisbon, for the first time, a session dedicated to award the winner of the Best Paper in CMBBE Journal will take place. We believe that CMBBE2018 will have a strong impact on the development of computational biomechanics and biomedical imaging and visualization, identifying emerging areas of research and promoting the collaboration and networking between participants. This impact is evidenced through the well-known research groups, commercial companies and scientific organizations, who continue to support and sponsor the CMBBE meeting series. In fact, the conference is enriched with five workshops on specific scientific topics and commercial software.info:eu-repo/semantics/draf

A versatile, automated and high-throughput drug screening platform for zebrafish embryos

Zebrafish provide a unique opportunity for drug screening in living animals, with the fast developing, transparent embryos allowing for relatively high-throughput, microscopy-based screens. However, the limited availability of rapid, flexible imaging and analysis platforms has limited the use of zebrafish in drug screens. We have developed an easy-to-use, customisable automated screening procedure suitable for high-throughput phenotype-based screens of live zebrafish. We utilised the WiScan® Hermes High Content Imaging System to rapidly acquire brightfield and fluorescent images of embryos, and the WiSoft® Athena Zebrafish Application for analysis, which harnesses an Artificial Intelligence-driven algorithm to automatically detect fish in brightfield images, identify anatomical structures, partition the animal into regions, and exclusively select the desired side-oriented fish. Our initial validation combined structural analysis with fluorescence images to enumerate GFP-tagged haematopoietic stem and progenitor cells in the tails of embryos, which correlated with manual counts. We further validated this system to assess the effects of genetic mutations and x-ray irradiation in high content using a wide range of assays. Further, we performed simultaneous analysis of multiple cell types using dual fluorophores in high throughput. In summary, we demonstrate a broadly applicable and rapidly customisable platform for high-content screening in zebrafish

Detection of osteoporosis in lumbar spine [L1-L4] trabecular bone: a review article

The human bones are categorized based on elemental micro architecture and porosity. The porosity of the inner trabecular bone is high that is 40-95% and the nature of the bone is soft and spongy where as the cortical bone is harder and is less porous that is 5 to 15%. Osteoporosis is a disease that normally affects women usually after their menopause. It largely causes mild bone fractures and further stages lead to the demise of an individual. This analysis is on the basis of bone mineral density (BMD) standards obtained through a variety of scientific methods experimented from different skeletal regions. The detection of osteoporosis in lumbar spine has been widely recognized as a promising way to frequent fractures. Therefore, premature analysis of osteoporosis will estimate the risk of the bone fracture which prevents life threats. This paper focuses on the advanced technology in imaging systems and fracture probability analysis of osteoporosis detection. The various segmentation techniques are explored to examine osteoporosis in particular region of the image and further significant attributes are extracted using different methods to classify normal and abnormal (osteoporotic) bones. The limitations of the reviewed papers are more in feature dimensions, lesser accuracy and expensive imaging modalities like computed tomography (CT), magnetic resonance imaging (MRI), and DEXA. To overcome these limitations it is suggested to have less feature dimensions, more accuracy and cost-effective imaging modality like X-ray. This is required to avoid bone fractures and to improve BMD with precision which further helps in the diagnosis of osteoporosis

Synergistic Visualization And Quantitative Analysis Of Volumetric Medical Images

The medical diagnosis process starts with an interview with the patient, and continues with the physical exam. In practice, the medical professional may require additional screenings to precisely diagnose. Medical imaging is one of the most frequently used non-invasive screening methods to acquire insight of human body. Medical imaging is not only essential for accurate diagnosis, but also it can enable early prevention. Medical data visualization refers to projecting the medical data into a human understandable format at mediums such as 2D or head-mounted displays without causing any interpretation which may lead to clinical intervention. In contrast to the medical visualization, quantification refers to extracting the information in the medical scan to enable the clinicians to make fast and accurate decisions. Despite the extraordinary process both in medical visualization and quantitative radiology, efforts to improve these two complementary fields are often performed independently and synergistic combination is under-studied. Existing image-based software platforms mostly fail to be used in routine clinics due to lack of a unified strategy that guides clinicians both visually and quan- titatively. Hence, there is an urgent need for a bridge connecting the medical visualization and automatic quantification algorithms in the same software platform. In this thesis, we aim to fill this research gap by visualizing medical images interactively from anywhere, and performing a fast, accurate and fully-automatic quantification of the medical imaging data. To end this, we propose several innovative and novel methods. Specifically, we solve the following sub-problems of the ul- timate goal: (1) direct web-based out-of-core volume rendering, (2) robust, accurate, and efficient learning based algorithms to segment highly pathological medical data, (3) automatic landmark- ing for aiding diagnosis and surgical planning and (4) novel artificial intelligence algorithms to determine the sufficient and necessary data to derive large-scale problems