RESUMEN
We introduce an automated method for the 3D tracking of carotids acquired as a sequence of 2D ultrasound images. The method includes an image stabilization step that compensates for the cardiac and respiratory motion of the carotid, and tracks the carotid wall via a shape and appearance model trained from representative images. Envisaging an application in automatic detection of plaques, the algorithm was tested on ultrasound volumes from 4,000 patients and its accuracy was evaluated by measuring the distance between the location of more than 4,000 carotid plaques and the location of the carotid wall as estimated by the proposed algorithm. Results show that the centroids of over 95% of the carotid plaques in the dataset were located within 3 mm of the estimated carotid wall, indicating the accuracy of the tracking algorithm.
Asunto(s)
Algoritmos , Inteligencia Artificial , Estenosis Carotídea/diagnóstico por imagen , Interpretación de Imagen Asistida por Computador/métodos , Imagenología Tridimensional/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Ultrasonografía/métodos , Humanos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Registration is one of the most common tasks of image analysis and computer vision applications. The requirements of most registration algorithms include large capture range and fast computation so that the algorithms are robust to different scenarios and can be computed in a reasonable amount of time. For these purposes, registration in the Fourier domain using normalized cross-correlation is well suited and has been extensively studied in the literature. Another common requirement is masking, which is necessary for applications where certain regions of the image that would adversely affect the registration result should be ignored. To address these requirements, we have derived a mathematical model that describes an exact form for embedding the masking step fully into the Fourier domain so that all steps of translation registration can be computed efficiently using Fast Fourier Transforms. We provide algorithms and implementation details that demonstrate the correctness of our derivations. We also demonstrate how this masked FFT registration approach can be applied to improve the Fourier-Mellin algorithm that calculates translation, rotation, and scale in the Fourier domain. We demonstrate the computational efficiency, advantages, and correctness of our algorithm on a number of images from real-world applications. Our framework enables fast, global, parameter-free registration of images with masked regions.
Asunto(s)
Algoritmos , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Técnica de Sustracción , Análisis de Fourier , Reproducibilidad de los Resultados , Sensibilidad y Especificidad , Procesamiento de Señales Asistido por ComputadorRESUMEN
A growing number of screening applications require the automated monitoring of cell populations in a high-throughput, high-content environment. These applications depend on accurate cell tracking of individual cells that display various behaviors including mitosis, merging, rapid movement, and entering and leaving the field of view. Many approaches to cell tracking have been developed in the past, but most are quite complex, require extensive post-processing, and are parameter intensive. To overcome such issues, we present a general, consistent, and extensible tracking approach that explicitly models cell behaviors in a graph-theoretic framework. We introduce a way of extending the standard minimum-cost flow algorithm to account for mitosis and merging events through a coupling operation on particular edges. We then show how the resulting graph can be efficiently solved using algorithms such as linear programming to choose the edges of the graph that observe the constraints while leading to the lowest overall cost. This tracking algorithm relies on accurate denoising and segmentation steps for which we use a wavelet-based approach that is able to accurately segment cells even in images with very low contrast-to-noise. In addition, the framework is able to measure and correct for microscope defocusing and stage shift. We applied the algorithms on nearly 6000 images of 400,000 cells representing 32,000 tracks taken from five separate datasets, each composed of multiple wells. Our algorithm was able to segment and track cells and detect different cell behaviors with an accuracy of over 99%. This overall framework enables accurate quantitative analysis of cell events and provides a valuable tool for high-throughput biological studies.
Asunto(s)
Algoritmos , Rastreo Celular/métodos , Citometría de Flujo/métodos , Interpretación de Imagen Asistida por Computador/métodos , Microscopía por Video/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
In this work, we address the problem of automated measurement of the interventricular septum thickness, one of the key parameters in cardiology, from B-mode echocardiograms. The problem is challenging due to high levels of noise, multi modal intensity, weak contrast due to near field haze, and non rigid motion of the septum across frames. We introduce a complete system for automated measurement of septum thickness from B-mode echocardiograms incorporating three main components: a 1D curve evolution algorithm using region statistics for segmenting the septum, a motion clustering method to locate the mitral valve, and a robust method to calculate the septum width from these inputs in accordance with medical standards. Our method effectively handles the challenges of such measurements and runs in near real time. Results on 57 patient recordings showed excellent agreement of the automated measurements with expert manual measurements.
Asunto(s)
Algoritmos , Ecocardiografía/métodos , Tabiques Cardíacos/diagnóstico por imagen , Ventrículos Cardíacos/diagnóstico por imagen , Interpretación de Imagen Asistida por Computador/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Humanos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Image focus quality is of utmost importance in digital microscopes because the pathologist cannot accurately characterize the tissue state without focused images. We propose to train a classifier to measure the focus quality of microscopy scans based on an extensive set of image features. However, classifiers rely heavily on the quality and quantity of the training data, and collecting annotated data is tedious and expensive. We therefore propose a new method to automatically generate large amounts of training data using image stacks. Our experiments demonstrate that a classifier trained with the image stacks performs comparably with one trained with manually annotated data. The classifier is able to accurately detect out-of-focus regions, provide focus quality feedback to the user, and identify potential problems of the microscopy design.
Asunto(s)
Algoritmos , Inteligencia Artificial , Aumento de la Imagen/métodos , Interpretación de Imagen Asistida por Computador/métodos , Microscopía/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
A growing number of screening applications require the automated monitoring of cell populations in a high-throughput, high-content environment. These applications depend on accurate cell tracking of individual cells that display various behaviors including mitosis, occlusion, rapid movement, and entering and leaving the field of view. We present a tracking approach that explicitly models each of these behaviors and represents the association costs in a graph-theoretic minimum-cost flow framework. We show how to extend the minimum-cost flow algorithm to account for mitosis and merging events by coupling particular edges. We applied the algorithm to nearly 6,000 images of 400,000 cells representing 32,000 tracks taken from five separate datasets, each composed of multiple wells. Our algorithm is able to track cells and detect different cell behaviors with an accuracy of over 99%.
Asunto(s)
Algoritmos , Inteligencia Artificial , Células Cultivadas/citología , Citometría de Flujo/métodos , Interpretación de Imagen Asistida por Computador/métodos , Microscopía/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Enabled by novel molecular markers, fluorescence microscopy enables the monitoring of multiple cellular functions using live cell assays. Automated image analysis is necessary to monitor such model systems in a high-throughput and high-content environment. Here, we demonstrate the ability to simultaneously track cell cycle phase and cell motion at the single cell level. Using a recently introduced cell cycle marker, we present a set of image analysis tools for automated cell phase analysis of live cells over extended time periods. Our model-based approach enables the characterization of the four phases of the cell cycle G1, S, G2, and M, which enables the study of the effect of inhibitor compounds that are designed to block the replication of cancerous cells in any of the phases. We approach the tracking problem as a spatio-temporal volume segmentation task, where the 2D slices are stacked into a volume with time as the z dimension. The segmentation of the G2 and S phases is accomplished using level sets, and we designed a model-based shape/size constraint to control the evolution of the level set. Our main contribution is the design of a speed function coupled with a fast marching path planning approach for tracking cells across the G1 phase based on the appearance change of the nuclei. The viability of our approach is demonstrated by presenting quantitative results on both controls and cases in which cells are treated with a cell cycle inhibitor.
Asunto(s)
Algoritmos , Ciclo Celular/fisiología , Células Cultivadas/citología , Interpretación de Imagen Asistida por Computador/métodos , Microscopía Fluorescente/métodos , Reconocimiento de Normas Patrones Automatizadas/métodos , Grabación en Video/métodos , Aumento de la Imagen/métodos , Reproducibilidad de los Resultados , Sensibilidad y EspecificidadRESUMEN
Emphysema is characterized by the destruction and over distension of lung tissue, which manifest on high resolution computer tomography (CT) images as regions of low attenuation. Typically, it is diagnosed by clinical symptoms, physical examination, pulmonary function tests, and X-ray and CT imaging. In this paper we discuss a quantitative imaging approach to analyze emphysema which employs low-level segmentations of CT images that partition the data into perceptually relevant regions. We constructed multi-dimensional histograms of feature values computed over the image segmentation. For each region in the segmentation, we derive a rich set of feature measurements. While we can use any combination of physical and geometric features, we found that limiting the scope to two features - the mean attenuation across a region and the region area - is effective. The subject histogram is compared to a set of canonical histograms representative of various stages of emphysema using the Earth Mover's Distance metric. Disease severity is assigned based on which canonical histogram is most similar to the subject histogram. Experimental results with 81 cases of emphysema at different stages of disease progression show good agreement against the reading of an expert radiologist.