Autism spectrum disorder characterization in children by capturing local-regional brain changes in MRI.

PURPOSE: To design a multiscale descriptor capable of capturing complex local-regional unfolding patterns to support quantitation and diagnosis of autism spectrum disorders (ASD) using T1-weighted structural magnetic resonance images (MRI) with voxel size of 1 × 1 × 1 mm. METHODS: The proposed image descriptor uses an adapted multiscale representation, the Curvelet transform, interpretable in terms of texture (local) and shape (regional) to characterize brain regions, and a Generalized Gaussian Distribution (GGD) to reduce feature dimensionality. In this approach, each MRI is first parcelled into 3D anatomical regions. Each resultant region is represented by a single 2D image where slices are placed next to each other. Each 2D image is characterized by mapping it to the Curvelet space and each of the different Curvelet sub-bands is described by the set of GGD parameters. To assess the discriminant power of the proposed descriptor, a classification model per brain region was built to differentiate ASD patients from control subjects. Models were constructed with support vector machines and evaluated using two samples from heterogeneous databases, namely Autism Brain Imaging Data Exchange - ABIDE I (34 ASD and 34 controls, mean age 11.46 ± 2.03 and 11.53 ± 1.79 yr, respectively, male population) and ABIDE II (42 ASD and 41 controls, mean age 10.09 ± 1.37 and 10.52 ± 1.27 yr, respectively, male population), for a total of 151 individuals. RESULTS: When the model was trained with ABIDE II sample and tested with ABIDE I on a hold-out validation, an area under receiver operator curve (AUC) of 0.69 was computed. When each sample was independently used under a cross-validation scheme, the estimated AUC was 0.75 ± 0.02 for ABIDE I and 0.77 ± 0.01 for ABIDE II. This analysis determined a set of discriminant regions widely reported in the literature as characteristic of ASD. CONCLUSIONS: The presented image descriptor demonstrated differences at local and regional level when high differences were observed in the Curvelet sub-bands. The method is simple in conceptual terms, robust to several sources of noise, and has a very low computational cost.

Automatic segmentation of right ventricle in cardiac cine MR images using a saliency analysis.

PURPOSE: Accurate measurement of the right ventricle (RV) volume is important for the assessment of the ventricular function and a biomarker of the progression of any cardiovascular disease. However, the high RV variability makes difficult a proper delineation of the myocardium wall. This paper introduces a new automatic method for segmenting the RV volume from short axis cardiac magnetic resonance (MR) images by a salient analysis of temporal and spatial observations. METHODS: The RV volume estimation starts by localizing the heart as the region with the most coherent motion during the cardiac cycle. Afterward, the ventricular chambers are identified at the basal level using the isodata algorithm, the right ventricle extracted, and its centroid computed. A series of radial intensity profiles, traced from this centroid, is used to search a salient intensity pattern that models the inner-outer myocardium boundary. This process is iteratively applied toward the apex, using the segmentation of the previous slice as a regularizer. The consecutive 2D segmentations are added together to obtain the final RV endocardium volume that serves to estimate also the epicardium. RESULTS: Experiments performed with a public dataset, provided by the RV segmentation challenge in cardiac MRI, demonstrated that this method is highly competitive with respect to the state of the art, obtaining a Dice score of 0.87, and a Hausdorff distance of 7.26 mm while a whole volume was segmented in about 3 s. CONCLUSIONS: The proposed method provides an useful delineation of the RV shape using only the spatial and temporal information of the cine MR images. This methodology may be used by the expert to achieve cardiac indicators of the right ventricle function.

Automatic detection of abnormal vascular cross-sections based on density level detection and support vector machines.

PURPOSE: The goal is to automatically detect anomalous vascular cross-sections to attract the radiologist's attention to possible lesions and thus reduce the time spent to analyze the image volume. MATERIALS AND METHODS: We assume that both lesions and calcifications can be considered as local outliers compared to a normal cross-section. Our approach uses an intensity metric within a machine learning scheme to differentiate normal and abnormal cross-sections. It is formulated as a Density Level Detection problem and solved using a Support Vector Machine (DLD-SVM). The method has been evaluated on 42 synthetic phantoms and on 9 coronary CT data sets annotated by 2 experts. RESULTS: The specificity of the method was 97.57% on synthetic data, and 86.01% on real data, while its sensitivity was 82.19 and 81.23%, respectively. The agreement with the observers, measured by the kappa coefficient, was substantial (κ = 0.72). After the learning stage, which is performed off-line, the average processing time was within 10 s per artery. CONCLUSIONS: To our knowledge, this is the first attempt to use the DLD-SVM approach to detect vascular abnormalities. Good specificity, sensitivity and agreement with experts, as well as a short processing time, show that our method can facilitate medical diagnosis and reduce evaluation time by attracting the reader's attention to suspect regions.

Adaptación del llgoritmo maracas para segmentacón de la arteria carótida y cuaantificación de estenosis en imágenes aac / Adaptation of the MARACAS Algorithm for Carotid Artery Segmentation and Stenosis Quantification on CT Images

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%.

Mejoramiento de la segmentación arterial coronaria a través del modelamiento del volumen parcial en imágenes de tomografía computarizada / Coronar y arter y segmentation improvement thro ugh partial volume modeling in CT images

En este artículo se propone un modelo estadístico de volumen parcial (VP) para mejorar la segmentación 3D de imágenes de tomografía computarizada (TC) cardiaca. Los efectos causados por el VP representan un reto en la separación arterial de las cavidades cardiacas, porque causan desbordamientos y segmentaciones erróneas. La propuesta incluye un campo aleatorio de Markov junto con un esquema de pesos modificado. Además, se utilizaron fantasmas sintéticos para evaluar la precisión del método, así como para determinar los parámetros de configuración ideales. Se usaron las imágenes de ocho pacientes, a fin de evaluar el método sobre datos reales, y se comparó el desempeño del esquema de pesos modificado con el esquema tradicional. También se demostró la capacidad del método para mejorar la segmentación cuando se usa en conjunto con un algoritmo de extracción de la línea central arterial.

In this article it is proposed a statistic model of Partial Volume (VP in spanish) to improve the 3D segmentation of Heart CT images. The effects caused by the VP represent a challenge in the arterial separation of the heart cavities because they cause overflowing and wrong segmentations. The proposal includes a random Markov field along to a modified weight scheme. Besides, synthetic ghosts where used to asses the precision of the method as well as to determine the parameters of ideal settings. The images of eight patients were used to evaluate the method based on real data and the performance of the modified weight scheme was compared with the traditional scheme. The ability of the method to improve the segmentation was proved when it was used along with a central arterial line extraction algorithm.