Discriminant snakes for 3D reconstruction of anatomical organs

Ultrasound image segmentation: a survey

This paper reviews ultrasound segmentation paper methods, in a broad sense, focusing on techniques developed for medical B-mode ultrasound images. First, we present a review of articles by clinical application to highlight the approaches that have been investigated and degree of validation that has been done in different clinical domains. Then, we present a classification of methodology in terms of use of prior information. We conclude by selecting ten papers which have presented original ideas that have demonstrated particular clinical usefulness or potential specific to the ultrasound segmentation problem.

Statistical strategy for anisotropic adventitia modelling in IVUS

Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders.

In vitro analysis of the cement mantle of femoral hip implants: development and validation of a CT-scan based measurement tool

We developed, validated and assessed inter- and intraobserver reliability of a CT-scan based measurement tool to evaluate morphological characteristics of the bone-cement-stem complex of hip implants in cadaver femurs. Two different models were investigated: the stem-cavity model using a double tapered polished femoral-stem that is removed after cement curing and the plastic-replica model using a stereolithographic stem replica that is left in place during CT-scanning. Software was developed to segment and analyze connective CT-images and identify the contours of bone, cement, and stem based on their respective gray values. Volume parameters (whole specimen, cement, stem, air contents of bone and cement), concentricity parameters (distances between centroids of stem and cement, cement and bone, stem and bone), contact surfaces (bone/air and cement/bone) and bone cement mantle thickness parameters were calculated. A three-dimensional protocol was developed to evaluate the minimal mantle thickness out of the CT-plane. The average accuracy for surfaces within CT-images was 7.47 mm2 (1.80%), for bone and cement mantle thickness it was 0.51 mm (9.39%), for distances between centroids it was 0.38 mm (18.5%) and contours: 0.27 mm (2.57%). The intra- and interobserver reliability of air content in bone and cement was sub-optimal (intraclass-correlation coefficient (ICC) as low as 0.54 with an average ICC of 0.85). All other variables were reliable (ICC>0.81, average ICC: 0.96). This in vitro technique can assess characteristics of cement mantles produced by different cementing techniques, stem types or centralizers.

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.