Towards Smarter Fluorescence Microscopy: Enabling Adaptive Acquisition Strategies With Optimized Photon Budget

Fluorescence microscopy is an invaluable technique for studying the intricate process of organism development. The acquisition process, however, is associated with the fundamental trade-off between the quality and reliability of the acquired data. On one hand, the goal of capturing the development in its entirety, often times across multiple spatial and temporal scales, requires extended acquisition periods. On the other hand, high doses of light required for such experiments are harmful for living samples and can introduce non-physiological artifacts in the normal course of development. Conventionally, a single set of acquisition parameters is chosen in the beginning of the acquisition and constitutes the experimenter’s best guess of the overall optimal configuration within the aforementioned trade-off. In the paradigm of adaptive microscopy, in turn, one aims at achieving more efficient photon budget distribution by dynamically adjusting the acquisition parameters to the changing properties of the sample. In this thesis, I explore the principles of adaptive microscopy and propose a range of improvements for two real imaging scenarios. Chapter 2 summarizes the design and implementation of an adaptive pipeline for efficient observation of the asymmetrically dividing neurogenic progenitors in Zebrafish retina. In the described approach the fast and expensive acquisition mode is automatically activated only when the mitotic cells are present in the field of view. The method illustrates the benefits of the adaptive acquisition in the common scenario of the individual events of interest being sparsely distributed throughout the duration of the acquisition. Chapter 3 focuses on computational aspects of segmentation-based adaptive schemes for efficient acquisition of the developing Drosophila pupal wing. Fast sample segmentation is shown to provide a valuable output for the accurate evaluation of the sample morphology and dynamics in real time. This knowledge proves instrumental for adjusting the acquisition parameters to the current properties of the sample and reducing the required photon budget with minimal effects to the quality of the acquired data. Chapter 4 addresses the generation of synthetic training data for learning-based methods in bioimage analysis, making them more practical and accessible for smart microscopy pipelines. State-of-the-art deep learning models trained exclusively on the generated synthetic data are shown to yield powerful predictions when applied to the real microscopy images. In the end, in-depth evaluation of the segmentation quality of both real and synthetic data-based models illustrates the important practical aspects of the approach and outlines the directions for further research

Visual Analytics and Interactive Machine Learning for Human Brain Data

Indiana University-Purdue University Indianapolis (IUPUI)This study mainly focuses on applying visualization techniques on human brain data for data exploration, quality control, and hypothesis discovery. It mainly consists of two parts: multi-modal data visualization and interactive machine learning. For multi-modal data visualization, a major challenge is how to integrate structural, functional and connectivity data to form a comprehensive visual context. We develop a new integrated visualization solution for brain imaging data by combining scientific and information visualization techniques within the context of the same anatomic structure. For interactive machine learning, we propose a new visual analytics approach to interactive machine learning. In this approach, multi-dimensional data visualization techniques are employed to facilitate user interactions with the machine learning process. This allows dynamic user feedback in different forms, such as data selection, data labeling, and data correction, to enhance the efficiency of model building

Raster Time Series: Learning and Processing

As the amount of remote sensing data is increasing at a high rate, due to great improvements in sensor technology, efficient processing capabilities are of utmost importance. Remote sensing data from satellites is crucial in many scientific domains, like biodiversity and climate research. Because weather and climate are of particular interest for almost all living organisms on earth, the efficient classification of clouds is one of the most important problems. Geostationary satellites such as Meteosat Second Generation (MSG) offer the only possibility to generate long-term cloud data sets with high spatial and temporal resolution. This work, therefore, addresses research problems on efficient and parallel processing of MSG data to enable new applications and insights. First, we address the lack of a suitable processing chain to generate a long-term Fog and Low Stratus (FLS) time series. We present an efficient MSG data processing chain that processes multiple tasks simultaneously, and raster data in parallel using the Open Computing Language (OpenCL). The processing chain delivers a uniform FLS classification that combines day and night approaches in a single method. As a result, it is possible to calculate a year of FLS rasters quite easy. The second topic presents the application of Convolutional Neural Networks (CNN) for cloud classification. Conventional approaches to cloud detection often only classify single pixels and ignore the fact that clouds are highly dynamic and spatially continuous entities. Therefore, we propose a new method based on deep learning. Using a CNN image segmentation architecture, the presented Cloud Segmentation CNN (CS-CNN) classifies all pixels of a scene simultaneously. We show that CS-CNN is capable of processing multispectral satellite data to identify continuous phenomena such as highly dynamic clouds. The proposed approach provides excellent results on MSG satellite data in terms of quality, robustness, and runtime, in comparison to Random Forest (RF), another widely used machine learning method. Finally, we present the processing of raster time series with a system for Visualization, Transformation, and Analysis (VAT) of spatio-temporal data. It enables data-driven research with explorative workflows and uses time as an integral dimension. The combination of various raster and vector data time series enables new applications and insights. We present an application that combines weather information and aircraft trajectories to identify patterns in bad weather situations

AnyStar: Domain randomized universal star-convex 3D instance segmentation

Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units. Existing instance segmentation networks for such structures train on densely labeled instances for each dataset, which requires substantial and often impractical manual annotation effort. Further, significant reengineering or finetuning is needed when presented with new datasets and imaging modalities due to changes in contrast, shape, orientation, resolution, and density. We present AnyStar, a domain-randomized generative model that simulates synthetic training data of blob-like objects with randomized appearance, environments, and imaging physics to train general-purpose star-convex instance segmentation networks. As a result, networks trained using our generative model do not require annotated images from unseen datasets. A single network trained on our synthesized data accurately 3D segments C. elegans and P. dumerilii nuclei in fluorescence microscopy, mouse cortical nuclei in micro-CT, zebrafish brain nuclei in EM, and placental cotyledons in human fetal MRI, all without any retraining, finetuning, transfer learning, or domain adaptation. Code is available at https://github.com/neel-dey/AnyStar.Comment: Code available at https://github.com/neel-dey/AnySta

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

Novel high performance techniques for high definition computer aided tomography

Mención Internacional en el título de doctorMedical image processing is an interdisciplinary field in which multiple research areas are involved: image acquisition, scanner design, image reconstruction algorithms, visualization, etc. X-Ray Computed Tomography (CT) is a medical imaging modality based on the attenuation suffered by the X-rays as they pass through the body. Intrinsic differences in attenuation properties of bone, air, and soft tissue result in high-contrast images of anatomical structures. The main objective of CT is to obtain tomographic images from radiographs acquired using X-Ray scanners. The process of building a 3D image or volume from the 2D radiographs is known as reconstruction. One of the latest trends in CT is the reduction of the radiation dose delivered to patients through the decrease of the amount of acquired data. This reduction results in artefacts in the final images if conventional reconstruction methods are used, making it advisable to employ iterative reconstruction algorithms. There are numerous reconstruction algorithms available, from which we can highlight two specific types: traditional algorithms, which are fast but do not enable the obtaining of high quality images in situations of limited data; and iterative algorithms, slower but more reliable when traditional methods do not reach the quality standard requirements. One of the priorities of reconstruction is the obtaining of the final images in near real time, in order to reduce the time spent in diagnosis. To accomplish this objective, new high performance techniques and methods for accelerating these types of algorithms are needed. This thesis addresses the challenges of both traditional and iterative reconstruction algorithms, regarding acceleration and image quality. One common approach for accelerating these algorithms is the usage of shared-memory and heterogeneous architectures. In this thesis, we propose a novel simulation/reconstruction framework, namely FUX-Sim. This framework follows the hypothesis that the development of new flexible X-ray systems can benefit from computer simulations, which may also enable performance to be checked before expensive real systems are implemented. Its modular design abstracts the complexities of programming for accelerated devices to facilitate the development and evaluation of the different configurations and geometries available. In order to obtain near real execution times, low-level optimizations for the main components of the framework are provided for Graphics Processing Unit (GPU) architectures. Other alternative tackled in this thesis is the acceleration of iterative reconstruction algorithms by using distributed memory architectures. We present a novel architecture that unifies the two most important computing paradigms for scientific computing nowadays: High Performance Computing (HPC). The proposed architecture combines Big Data frameworks with the advantages of accelerated computing. The proposed methods presented in this thesis provide more flexible scanner configurations as they offer an accelerated solution. Regarding performance, our approach is as competitive as the solutions found in the literature. Additionally, we demonstrate that our solution scales with the size of the problem, enabling the reconstruction of high resolution images.El procesamiento de imágenes médicas es un campo interdisciplinario en el que participan múltiples áreas de investigación como la adquisición de imágenes, diseño de escáneres, algoritmos de reconstrucción de imágenes, visualización, etc. La tomografía computarizada (TC) de rayos X es una modalidad de imágen médica basada en el cálculo de la atenuación sufrida por los rayos X a medida que pasan por el cuerpo a escanear. Las diferencias intrínsecas en la atenuación de hueso, aire y tejido blando dan como resultado imágenes de alto contraste de estas estructuras anatómicas. El objetivo principal de la TC es obtener imágenes tomográficas a partir estas radiografías obtenidas mediante escáneres de rayos X. El proceso de construir una imagen o volumen en 3D a partir de las radiografías 2D se conoce como reconstrucción. Una de las últimas tendencias en la tomografía computarizada es la reducción de la dosis de radiación administrada a los pacientes a través de la reducción de la cantidad de datos adquiridos. Esta reducción da como resultado artefactos en las imágenes finales si se utilizan métodos de reconstrucción convencionales, por lo que es aconsejable emplear algoritmos de reconstrucción iterativos. Existen numerosos algoritmos de reconstrucción disponibles a partir de los cuales podemos destacar dos categorías: algoritmos tradicionales, rápidos pero no permiten obtener imágenes de alta calidad en situaciones en las que los datos son limitados; y algoritmos iterativos, más lentos pero más estables en situaciones donde los métodos tradicionales no alcanzan los requisitos en cuanto a la calidad de la imagen. Una de las prioridades de la reconstrucción es la obtención de las imágenes finales en tiempo casi real, con el fin de reducir el tiempo de diagnóstico. Para lograr este objetivo, se necesitan nuevas técnicas y métodos de alto rendimiento para acelerar estos algoritmos. Esta tesis aborda los desafíos de los algoritmos de reconstrucción tradicionales e iterativos, con respecto a la aceleración y la calidad de imagen. Un enfoque común para acelerar estos algoritmos es el uso de arquitecturas de memoria compartida y heterogéneas. En esta tesis, proponemos un nuevo sistema de simulación/reconstrucción, llamado FUX-Sim. Este sistema se construye alrededor de la hipótesis de que el desarrollo de nuevos sistemas de rayos X flexibles puede beneficiarse de las simulaciones por computador, en los que también se puede realizar un control del rendimiento de los nuevos sistemas a desarrollar antes de su implementación física. Su diseño modular abstrae las complejidades de la programación para aceleradores con el objetivo de facilitar el desarrollo y la evaluación de las diferentes configuraciones y geometrías disponibles. Para obtener ejecuciones en casi tiempo real, se proporcionan optimizaciones de bajo nivel para los componentes principales del sistema en las arquitecturas GPU. Otra alternativa abordada en esta tesis es la aceleración de los algoritmos de reconstrucción iterativa mediante el uso de arquitecturas de memoria distribuidas. Presentamos una arquitectura novedosa que unifica los dos paradigmas informáticos más importantes en la actualidad: computación de alto rendimiento (HPC) y Big Data. La arquitectura propuesta combina sistemas Big Data con las ventajas de los dispositivos aceleradores. Los métodos propuestos presentados en esta tesis proporcionan configuraciones de escáner más flexibles y ofrecen una solución acelerada. En cuanto al rendimiento, nuestro enfoque es tan competitivo como las soluciones encontradas en la literatura. Además, demostramos que nuestra solución escala con el tamaño del problema, lo que permite la reconstrucción de imágenes de alta resolución.This work has been mainly funded thanks to a FPU fellowship (FPU14/03875) from the Spanish Ministry of Education. It has also been partially supported by other grants: • DPI2016-79075-R. “Nuevos escenarios de tomografía por rayos X”, from the Spanish Ministry of Economy and Competitiveness. • TIN2016-79637-P Towards unification of HPC and Big Data Paradigms from the Spanish Ministry of Economy and Competitiveness. • Short-term scientific missions (STSM) grant from NESUS COST Action IC1305. • TIN2013-41350-P, Scalable Data Management Techniques for High-End Computing Systems from the Spanish Ministry of Economy and Competitiveness. • RTC-2014-3028-1 NECRA Nuevos escenarios clinicos con radiología avanzada from the Spanish Ministry of Economy and Competitiveness.Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: José Daniel García Sánchez.- Secretario: Katzlin Olcoz Herrero.- Vocal: Domenico Tali

One DAG to Rule Them All

In this paper, we present novel strategies for optimizing the performance of many binary image processing algorithms. These strategies are collected in an open-source framework, GRAPHGEN, that is able to automatically generate optimized C++ source code implementing the desired optimizations. Simply starting from a set of rules, the algorithms introduced with the GRAPHGEN framework can generate decision trees with minimum average path-length, possibly considering image pattern frequencies, apply state prediction and code compression by the use of Directed Rooted Acyclic Graphs (DRAGs). Moreover, the proposed algorithmic solutions allow to combine different optimization techniques and significantly improve performance. Our proposal is showcased on three classical and widely employed algorithms (namely Connected Components Labeling, Thinning, and Contour Tracing). When compared to existing approaches —in 2D and 3D—, implementations using the generated optimal DRAGs perform significantly better than previous state-of-the-art algorithms, both on CPU and GPU

A Novel Fog Computing Approach for Minimization of Latency in Healthcare using Machine Learning

In the recent scenario, the most challenging requirements are to handle the massive generation of multimedia data from the Internet of Things (IoT) devices which becomes very difficult to handle only through the cloud. Fog computing technology emerges as an intelligent solution and uses a distributed environment to operate. The objective of the paper is latency minimization in e-healthcare through fog computing. Therefore, in IoT multimedia data transmission, the parameters such as transmission delay, network delay, and computation delay must be reduced as there is a high demand for healthcare multimedia analytics. Fog computing provides processing, storage, and analyze the data nearer to IoT and end-users to overcome the latency. In this paper, the novel Intelligent Multimedia Data Segregation (IMDS) scheme using Machine learning (k-fold random forest) is proposed in the fog computing environment that segregates the multimedia data and the model used to calculate total latency (transmission, computation, and network). With the simulated results, we achieved 92% as the classification accuracy of the model, an approximately 95% reduction in latency as compared with the pre-existing model, and improved the quality of services in e-healthcare

Parallel error-correcting output codes classification in volume visualization

In volume visualization, the definition of the regions of interest is inherently an iterative trial-and-error process finding out the best parameters to classify and render the final image. Generally, the user requires a lot of expertise to analyze and edit these parameters through multi-dimensional transfer functions. In this thesis, we present a framework of methods to label on-demand multiple regions of interest. The methods selected are a combination of 1vs1 Adaboost binary classifiers and an ECOC framework to combine binary results to generate a multi-class result. On a first step, Adaboost is used to train a set of 1vs1 binary classifiers, with a labeled subset of points on the target volume. On a second step, an ECOC framework is used to combine the Adaboost classifiers and classify the rest of the volume, assigning a label to each point among multiple possible labels. The labels have to be introduced by an expert on the target volume, and this labels have to be a small subset of all the points on the volume we want to classify. That way, we require a small e↵ort to the expert. But this requires an interactive process where the classification results are obtained in real or near real-time. That why on this master thesis we implemented the classification step in OpenCL, to exploit the parallelism in modern GPU. We provide experimental results for both accuracy on classification and execution time speedup, comparing GPU to single and multi-core CPU. Along with this work we will present some work derived from the use of OpenCL for the experiments, that we shared in OpenSource through Google code, and some abstraction on the parallelization process for any algorithm. Also, we will comment on future work and present some conclusions as the final sections of this document