RESUMO
The long-term goal of our study is to understand the internal organization of the octocoral stem canals, as well as their physiological and functional role in the growth of the colonies, and finally to assess the influence of climatic changes on this species. Here we focus on imaging tools, namely acquisition and processing of three-dimensional high-resolution images, with emphasis on automated extraction of canal pathways. Our aim was to evaluate the feasibility of the whole process, to point out and solve - if possible - technical problems related to the specimen conditioning, to determine the best acquisition parameters and to develop necessary image-processing algorithms. The pathways extracted are expected to facilitate the structural analysis of the colonies, namely to help observing the distribution, formation and number of canals along the colony. Five volumetric images of Muricea muricata specimens were successfully acquired by X-ray computed tomography with spatial resolution ranging from 4.5 to 25 micrometers. The success mainly depended on specimen immobilization. More than [Formula: see text] of the canals were successfully detected and tracked by the image-processing method developed. Thus obtained three-dimensional representation of the canal network was generated for the first time without the need of histological or other destructive methods. Several canal patterns were observed. Although most of them were simple, i.e. only followed the main branch or "turned" into a secondary branch, many others bifurcated or fused. A majority of bifurcations were observed at branching points. However, some canals appeared and/or ended anywhere along a branch. At the tip of a branch, all canals fused into a unique chamber. Three-dimensional high-resolution tomographic imaging gives a non-destructive insight to the coral ultrastructure and helps understanding the organization of the canal network. Advanced image-processing techniques greatly reduce human observer's effort and provide methods to both visualize and quantify the structures of interest.
Assuntos
Antozoários/anatomia & histologia , Imageamento Tridimensional , Algoritmos , Animais , Microtomografia por Raio-XRESUMO
En este artículo se describen las adaptaciones hechas al algoritmo MARACAS para segmentar y cuantificar estructuras vasculares en imágenes TAC de la arteria carótida. El algoritmo MARACAS, que está basado en un modelo elástico y en un análisis de los valores y vectores propios de la matriz de inercia, fue inicialmente diseñado para segmentar una sola arteria en imágenes ARM. Las modificaciones están principalmente enfocadas a tratar las especificidades de las imágenes TAC, así como la presencia de bifurcaciones. Los algoritmos implementados en esta nueva versión se clasifican en dos niveles. 1. Los procesamientos de bajo nivel (filtrado de ruido y de artificios direccionales, presegmentación y realce) destinados a mejorar la calidad de la imagen y presegmentarla. Estas técnicas están basadas en información a priori sobre el ruido, los artificios y los intervalos típicos de niveles de gris del lumen, del fondo y de las calcificaciones. 2. Los procesamientos de alto nivel para extraer la línea central de la arteria, segmentar el lumen y cuantificar la estenosis. A este nivel, se aplican conocimientos a priori sobre la forma y anatomía de las estructuras vasculares. El método fue evaluado en 31 imágenes suministradas en el concurso Carotid Lumen Segmentation and Stenosis Grading Grand Challenge 2009. Los resultados obtenidos en la segmentación arrojaron un coeficiente de similitud de Dice promedio de 80,4% comparado con la segmentación de referencia, y el error promedio de la cuantificación de estenosis fue 14,4%.
This paper describes the adaptations of MARACAS algorithm to the segmentation and quantification of vascular structures in CTA images of the carotid artery. The MARACAS algorithm, which is based on an elastic model and on a multi-scale eigen-analysis of the inertia matrix, was originally designed to segment a single artery in MRA images. The modifications are primarily aimed at addressing the specificities of CT images and the bifurcations. The algorithms implemented in this new version are classified into two levels. 1. The low-level processing (filtering of noise and directional artifacts, enhancement and pre-segmentation) to improve the quality of the image and to pre-segment it. These techniques are based on a priori information about noise, artifacts and typical gray levels ranges of lumen, background and calcifications. 2. The high-level processing to extract the centerline of the artery, to segment the lumen and to quantify the stenosis. At this level, we apply a priori knowledge of shape and anatomy of vascular structures. The method was evaluated on 31 datasets from the Carotid Lumen Segmentation and Stenosis Grading Grand Challenge 2009. The segmentation results obtained an average of 80:4% Dice similarity score, compared to reference segmentations, and the mean stenosis quantification error was 14.4%.