Reconstruction and web distribution of measurable arterial models

Reconstruction and finite element mesh generation of abdominal aortic aneurysms from computerized tomography angiography data with minimal user interactions

Evaluating rupture risk of abdominal aortic aneurysms is critically important in reducing related mortality without unnecessarily increasing the rate of elective repair. According to the current clinical practice aneurysm rupture risk is (mainly) estimated from its maximum diameter and/or expansion rate; an approach motivated from statistics but known to fail often in individuals. In contrast, recent research demonstrated that patient specific biomechanical simulations can provide more reliable diagnostic parameters, however current structural model development is cumbersome and time consuming. This paper used 2D and 3D deformable models to reconstruct aneurysms from computerized tomography angiography data with minimal user interactions. In particular, formulations of frames and shells, as known from structural mechanics, were used to define deformable modes, which in turn allowed a direct mechanical interpretation of the applied set of reconstruction parameters. Likewise, a parallel finite element implementation of the models allows the segmentation of clinical cases on standard personal computers within a few minutes. The particular topology of the applied 3D deformable models supports a fast and simple hexahedral-dominated meshing of the arising generally polyhedral domain. The variability of the derived segmentations (luminal: 0.50(SD 0.19) mm; exterior 0.89(SD 0.45) mm) with respect to large variations in elastic properties of the deformable models was in the range of the differences between manual segmentations as performed by experts (luminal: 0.57(SD 0.24) mm; exterior: 0.77(SD 0.58) mm), and was particularly independent from the algorithm's initialization. The proposed interaction of deformable models and mesh generation defines finite element meshes suitable to perform accurate and efficient structural analysis of the aneurysm using mixed finite element formulations.

Segmentation of thrombus in abdominal aortic aneurysms from CTA with nonparametric statistical grey level appearance modeling

This paper presents a new method for deformable model-based segmentation of lumen and thrombus in abdominal aortic aneurysms from computed tomography (CT) angiography (CTA) scans. First the lumen is segmented based on two positions indicated by the user, and subsequently the resulting surface is used to initialize the automated thrombus segmentation method. For the lumen, the image-derived deformation term is based on a simple grey level model (two thresholds). For the more complex problem of thrombus segmentation, a grey level modeling approach with a nonparametric pattern classification technique is used, namely k-nearest neighbors. The intensity profile sampled along the surface normal is used as classification feature. Manual segmentations are used for training the classifier: samples are collected inside, outside, and at the given boundary positions. The deformation is steered by the most likely class corresponding to the intensity profile at each vertex on the surface. A parameter optimization study is conducted, followed by experiments to assess the overall segmentation quality and the robustness of results against variation in user input. Results obtained in a study of 17 patients show that the agreement with respect to manual segmentations is comparable to previous values reported in the literature, with considerable less user interaction.

Abdominal aortic aneurysms: virtual imaging and analysis through a remote web server

The study describes the application of a web-based software in the planning of the endovascular treatment of abdominal aortic aneurysms (AAA). The software has been developed in the framework of a 2-year research project called Aneurysm QUAntification Through an Internet Collaborative System (AQUATICS); it allows to manage remotely Virtual Reality Modeling Language (VRML) models of the abdominal aorta, derived from multirow computed tomography angiography (CTA) data sets, and to obtain measurements of diameters, angles and centerline lengths. To test the reliability of measurements, two radiologists performed a detailed analysis of multiple 3D models generated from a synthetic phantom, mimicking an AAA. The system was tested on 30 patients with AAA; CTA data sets were mailed and the time required for segmentation and measurement were collected for each case. The Bland-Altman plot analysis showed that the mean intra- and inter-observer differences in measures on phantoms were clinically acceptable. The mean time required for segmentation was 1 h (range 45-120 min). The mean time required for measurements on the web was 7 min (range 4-11 min). The AQUATICS web server may provide a rapid, standardized and accurate tool for the evaluation of AAA prior to the endovascular treatment.

Interactive segmentation of abdominal aortic aneurysms in CTA images

A model-based approach to interactive segmentation of abdominal aortic aneurysms from CTA data is presented. After manual delineation of the aneurysm sac in the first slice, the method automatically detects the contour in subsequent slices, using the result from the previous slice as a reference. If an obtained contour is not sufficiently accurate, the user can intervene and provide an additional manual reference contour. The method is inspired by the active shape model (ASM) segmentation scheme (), in which a statistical shape model, derived from corresponding landmark points in manually labeled training images, is fitted to the image in an iterative manner. In our method, a shape model of the contours in two adjacent image slices is progressively fitted to the entire volume. The contour obtained in one slice thus constrains the possible shapes in the next slice. The optimal fit is determined on the basis of multi-resolution gray level models constructed from gray value patches sampled around each landmark. We propose to use the similarity of adjacent image slices for this gray level model, and compare these to single-slice features that are more generally used with ASM. The performance of various image features is evaluated in leave-one-out experiments on 23 data sets. Features that use the similarity of adjacent image slices outperform measures based on single-slice features in all cases. The average number of slices in our datasets is 51, while on average eight manual initializations are required, which decreases operator segmentation time by a factor of 6.