Accurate and Efficient Linear Structure Segmentation by Leveraging Ad Hoc Features with Learned Filters

Extracting linear structures, such as blood vessels or dendrites, from images is crucial in many medical imagery applications, and many handcrafted features have been proposed to solve this problem. However, such features rely on assumptions that are never entirely true. Learned features, on the other hand, can capture image characteristics difficult to define analytically, but tend to be much slower to compute than handcrafted features. We propose to complement handcrafted methods with features found using very recent Machine Learning techniques, and we show that even few filters are sufficient to efficiently leverage handcrafted features. We demonstrate our approach on the STARE, DRIVE, and BF2D datasets, and on 2D projections of neural images from the DIADEM challenge. Our proposal outperforms handcrafted methods, and pairs up with learning-only approaches at a fraction of their computational cost

Active Learning and Proofreading for Delineation of Curvilinear Structures

Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the topology of the final result. In this paper we propose a generic approach to addressing both of these problems by taking into account the influence of a potential misclassification on the resulting delineation. In an Active Learning context, we identify parts of linear structures that should be annotated first in order to train a classifier effectively. In a proofreading context, we similarly find regions of the resulting reconstruction that should be verified in priority to obtain a nearly-perfect result. In both cases, by focusing the attention of the human expert on potential classification mistakes which are the most critical parts of the delineation, we reduce the amount of required supervision. We demonstrate the effectiveness of our approach on microscopy images depicting blood vessels and neurons

A somatic coliphage threshold approach to improve the management of activated sludge wastewater treatment plant effluents in resource-limited regions

Versión aceptada para publicaciónEffective wastewater management is crucial to ensure the safety of water reuse projects and 29 effluent discharge into surface waters. Multiple studies have demonstrated that municipal 30 wastewater treatment with conventional activated sludge processes is inefficient for the removal 31 of the wide spectrum of viruses in sewage. In this study, a well-accepted statistical approach was 32 used to investigate the relationship between viral indicators and human enteric viruses during 33 wastewater treatment in a resource-limited region. Influent and effluent samples from five urban 34 wastewater treatment plants (WWTP) in Costa Rica were analyzed for somatic coliphage and 35 human enterovirus, hepatitis A virus, norovirus genotype I and II, and rotavirus. All WWTP 36 provide primary treatment followed by conventional activated sludge treatment prior to 37 discharge into surface waters that are indirectly used for agricultural irrigation. The results 38 revealed a statistically significant relationship between the detection of at least one of the five 39 human enteric viruses and somatic coliphage. Multiple logistic regression and Receiver Operating Characteristic curve analysis identified a threshold of 3.0 ×103 40 (3.5-log10) somatic 41 coliphage plaque forming unit per 100 mL, which corresponded to an increased likelihood of encountering enteric viruses above the limit of detection (>1.83×102 42 virus target/100 mL). 43 Additionally, quantitative microbial risk assessment was executed for famers indirectly reusing 44 WWTP effluent that met the proposed threshold. The resulting estimated median cumulative 45 annual disease burden complied with World Health Organization recommendations. Future 46 studies are needed to validate the proposed threshold for use in Costa Rica and other regions.Universidad de Costa Rica/[]/UCR/Costa RicaNational Science Foundation/[OCE-1566562]/NSF/Estados UnidosUCR::Vicerrectoría de Investigación::Unidades de Investigación::Ciencias de la Salud::Instituto de Investigaciones en Salud (INISA)UCR::Vicerrectoría de Docencia::Salud::Facultad de Microbiologí

Apertura económica y democracia México 1980-2002

Se realizó un estudio del índice de Democracia y las variables correspondientes a apertura económica en México, y algunas variables sociales para el período de 1980 a 2002, examinando la relación entre estas

Supervised Feature Learning for Curvilinear Structure Segmentation

Abstract. We present a novel, fully-discriminative method for curvilinear structure segmentation that simultaneously learns a classifier and the features it relies on. Our approach requires almost no parameter tuning and, in the case of 2D images, removes the requirement for handdesigned features, thus freeing the practitioner from the time-consuming tasks of parameter and feature selection. Our approach relies on the Gradient Boosting framework to learn discriminative convolutional filters in closed form at each stage, and can operate on raw image pixels as well as additional data sources, such as the output of other methods like the Optimally Oriented Flux. We will show that it outperforms state-of-theart curvilinear segmentation methods on both 2D images and 3D image stacks.

Towards Segmentation of Irregular Tubular Structures in 3D Confocal Microscope Images

Abstract — In this paper, we propose a general framework for learning and predicting tubular models in 3D images. By using Support Vector Machines we take advantage of the data transformation into a high dimensional space to estimate the posterior probability of an element belonging to a tube-like 3D object. This learning eliminates the need for performing multiscale analysis on the data. We compare the performance of our method with standard approaches for tubularity enhancement in both synthetic data and confocal 3D images. Our method achieves substantial improvements over classical methods. Index Terms — Support vector machines, machine learning, vessel enhancement, dendrite detection, generalized cylinders. I

Automatic Reconstruction of Dendrite

The function of the human brain arises from computations that occur within and among billions of nerve cells known as neurons. A neuron is composed primarily of a cell body (soma) from which emanates a collection of branching structures (dendrites). How neuronal signals are processed is dependent on the dendrites' specific morphology and distribution of voltage-gated ion channels. To understand this processing, it is necessary to acquire an accurate structural analysis of the cell. Toward this end, we present an automated reconstruction system which extracts the morphology of neurons imaged from confocal and multiphoton microscopes. As we place emphasis on this being a rapid (and therefore automated) process, we have developed several techniques that provide high-quality reconstructions with minimal human interaction. In addition to generating a tree of connected cylinders representing the reconstructed neuron, a computational model is also created for purposes of performing functional simulations. We present visual and statistical results from reconstructions performed both on real image volumes and on noised synthetic data from the Duke-Southampton archive