Automatic Abdominal Aortic Aneurysm segmentation in MR images

Abdominal Aortic Aneurism is a disease related to a weakening in the aortic wall that can cause a break in the aorta and the death. The detection of an unusual dilatation of a section of the aorta is an indicative of this disease. However, it is difficult to diagnose because it is necessary image diagnosis using computed tomography or magnetic resonance. An automatic diagnosis system would allow to analyze abdominal magnetic resonance images and to warn doctors if any anomaly is detected. We focus our research in magnetic resonance images because of the absence of ionizing radiation. Although there are proposals to identify this disease in magnetic resonance images, they need an intervention from clinicians to be precise and some of them are computationally hard. In this paper we develop a novel approach to analyze magnetic resonance abdominal images and detect the lumen and the aortic wall. The method combines different algorithms in two stages to improve the detection and the segmentation so it can be applied to similar problems with other type of images or structures. In a first stage, we use a spatial fuzzy C-means algorithm with morphological image analysis to detect and segment the lumen; and subsequently, in a second stage, we apply a graph cut algorithm to segment the aortic wall. The obtained results in the analyzed images are pretty successful obtaining an average of 79% of overlapping between the automatic segmentation provided by our method and the aortic wall identified by a medical specialist. The main impact of the proposed method is that it works in a completely automatic way with a low computational cost, which is of great significance for any expert and intelligent system.

Corneal topography reinterpretation through separate analysis of the projected rings

Póster presentado en SPIE Photonics Europe, Brussels, 16-19 April 2012.

Overexpression of guanylate cyclase activating protein 2 in rod photoreceptors in vivo leads to morphological changes at the synaptic ribbon

Guanylate cyclase activating proteins are EF-hand containing proteins that confer calcium sensitivity to retinal guanylate cyclase at the outer segment discs of photoreceptor cells. By making the rate of cGMP synthesis dependent on the free intracellular calcium levels set by illumination, GCAPs play a fundamental role in the recovery of the light response and light adaptation. The main isoforms GCAP1 and GCAP2 also localize to the synaptic terminal, where their function is not known. Based on the reported interaction of GCAP2 with Ribeye, the major component of synaptic ribbons, it was proposed that GCAP2 could mediate the synaptic ribbon dynamic changes that happen in response to light. We here present a thorough ultrastructural analysis of rod synaptic terminals in loss-of-function (GCAP1/GCAP2 double knockout) and gain-of-function (transgenic overexpression) mouse models of GCAP2. Rod synaptic ribbons in GCAPs−/− mice did not differ from wildtype ribbons when mice were raised in constant darkness, indicating that GCAPs are not required for ribbon early assembly or maturation. Transgenic overexpression of GCAP2 in rods led to a shortening of synaptic ribbons, and to a higher than normal percentage of club-shaped and spherical ribbon morphologies. Restoration of GCAP2 expression in the GCAPs−/− background (GCAP2 expression in the absence of endogenous GCAP1) had the striking result of shortening ribbon length to a much higher degree than overexpression of GCAP2 in the wildtype background, as well as reducing the thickness of the outer plexiform layer without affecting the number of rod photoreceptor cells. These results indicate that preservation of the GCAP1 to GCAP2 relative levels is relevant for maintaining the integrity of the synaptic terminal. Our demonstration of GCAP2 immunolocalization at synaptic ribbons at the ultrastructural level would support a role of GCAPs at mediating the effect of light on morphological remodeling changes of synaptic ribbons.

Morphological classification of microtidal sand and gravel beaches

A new classification of microtidal sand and gravel beaches with very different morphologies is presented below. In 557 studied transects, 14 variables were used. Among the variables to be emphasized is the depth of the Posidonia oceanica. The classification was performed for 9 types of beaches: Type 1: Sand and gravel beaches, Type 2: Sand and gravel separated beaches, Type 3: Gravel and sand beaches, Type 4: Gravel and sand separated beaches, Type 5: Pure gravel beaches, Type 6: Open sand beaches, Type 7: Supported sand beaches, Type 8: Bisupported sand beaches and Type 9: Enclosed beaches. For the classification, several tools were used: discriminant analysis, neural networks and Support Vector Machines (SVM), the results were then compared. As there is no theory for deciding which is the most convenient neural network architecture to deal with a particular data set, an experimental study was performed with different numbers of neuron in the hidden layer. Finally, an architecture with 30 neurons was chosen. Different kernels were employed for SVM (Linear, Polynomial, Radial basis function and Sigmoid). The results obtained for the discriminant analysis were not as good as those obtained for the other two methods (ANN and SVM) which showed similar success.