Vessel boundary extraction based on a global and local deformable physical model with variable stiffness

An attempt toward objective assessment of brain tumor vascularization using susceptibility weighted imaging and dedicated computer program - a preliminary study

Background:

Susceptibility weighted imaging (SWI) is a novel MRI sequence which demonstrates the susceptibility differences between adjacent tissues and it is promising to be a sequence useful in the assessment of brain tumors vascularity. The aim of our study was to demonstrate usefulness of SWI in evaluation of intratumoral vessels in comparison to CET1 sequence in a standardized, objective manner.

Material/Methods:

10 patients with supratentorial brain tumors were included in the study. All of them underwent conventional MRI examination with a 1,5 T scanner. SWI sequence was additionally performed using the following parameters: TR 49 ms,TE 40 ms. We used authors’ personal computer software – Vessels View, to assess the vessels number.

Results:

Comparison of SWI and CET1 sequences was performed using our program. Analysis of all 26 ROIs demonstrated predominance of SWI in the amount of white pixels (vessel cross-sectional) and a similar number of elongated structures (blood vessels).

Conclusions:

To conclude, the results of this study are encouraging; they confirm the added value of SWI as an appropriate and useful sequence in the process of evaluation of intratumoral vascularity. Using our program significantly improved visualization of blood vessels in cerebral tumors. The Vessel View application assists radiologists in demonstrating the vessels and facilitates distinguishing them from adjacent tissues in the image.

An abdominal aortic aneurysm segmentation method: level set with region and statistical information

We present a system for segmenting the human aortic aneurysm in CT angiograms (CTA), which, in turn, allows measurements of volume and morphological aspects useful for treatment planning. The system estimates a rough "initial surface," and then refines it using a level set segmentation scheme augmented with two external analyzers: The global region analyzer, which incorporates a priori knowledge of the intensity, volume, and shape of the aorta and other structures, and the local feature analyzer, which uses voxel location, intensity, and texture features to train and drive a support vector machine classifier. Each analyzer outputs a value that corresponds to the likelihood that a given voxel is part of the aneurysm, which is used during level set iteration to control the evolution of the surface. We tested our system using a database of 20 CTA scans of patients with aortic aneurysms. The mean and worst case values of volume overlap, volume error, mean distance error, and maximum distance error relative to human tracing were 95.3% +/- 1.4% (s.d.); worst case = 92.9%, 3.5% +/- 2.5% (s.d.); worst case = 7.0%, 0.6 +/- 0.2 mm (s.d.); worst case = 1.0 mm, and 5.2 +/- 2.3 mm (s.d.); worst case = 9.6 mm, respectively. When implemented on a 2.8 GHz Pentium IV personal computer, the mean time required for segmentation was 7.4 +/- 3.6 min (s.d.). We also performed experiments that suggest that our method is insensitive to parameter changes within 10% of their experimentally determined values. This preliminary study proves feasibility for an accurate, precise, and robust system for segmentation of the abdominal aneurysm from CTA data, and may be of benefit to patients with aortic aneurysms.

Branch cut surface placement for unwrapping of undersampled three-dimensional phase data: application to magnetic resonance imaging arterial flow mapping

We demonstrate in both simulated and real cases the effect that undersampling of a three-dimensional (3D) wrapped phase distribution has on the geometry of phase singularity loops and their branch cut surfaces. The more intuitive two-dimensional (2D) problem of setting branch cuts between dipole pairs is taken as a starting point, and then branch cut surfaces in flat and ambiguous 3D loops are discussed. It is shown that the correct 2D branch cuts and 3D branch cut surfaces should be placed where the gradient of the original phase distribution exceeded pi rad voxel(-1). This information, however, is lost owing to undersampling and cannot be recovered from the sampled wrapped phase distribution alone. As a consequence, empirical rules such as finding the surface of minimal area or methods based on the wrapped phase gradient will fail to find the correct branch cut surfaces. We conclude that additional information about the problem under study is therefore needed to produce correct branch cut surfaces that lead to an unwrapped phase distribution with minimum local errors. An example with real data is provided in which downsampled phase contrast magnetic resonance imaging data are successfully unwrapped when the position of the vessel walls and the physical properties of the flowing blood are taken into account.

A specialized plug-in software module for computer-aided quantitative measurement of medical images

This paper presents a specialized system for quantitative measurement of medical images. Using Visual C++, we developed a computer-aided software based on Image-Pro Plus (IPP), a software development platform. When transferred to the hard disk of a computer by an MVPCI-V3A frame grabber, medical images can be automatically processed by our own IPP plug-in for immunohistochemical analysis, cytomorphological measurement and blood vessel segmentation. In 34 clinical studies, the system has shown its high stability, reliability and ease of utility.

Optic nerve head segmentation

Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 microns/pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred images.

Automated vessel edge detection in velocity-encoded cine-MR (VEC-MR) flow measurements: a retrospective evaluation in critically ill patients

OBJECTIVE

To assess feasibility of automated edge detection in magnetic resonance (MR) flow calculations in a clinical setting with critically ill patients.

MATERIAL AND METHODS

Velocity encoded cine-MR (VEC-MR) flow measurements cross-sectional area (CSA), mean spatial velocity (MSV), instantaneous flow (IF), flow (F), 0.5 T Philips, TR 800-800, TE=8 ms, 30 degrees flip angle, FOV 280 mm, 128 x 256 matrix, temporal resolution 16 time frames/RR, VENC=120 cm/s) were obtained in 20 major thoracic human vessels (ascending aorta, main, right and left pulmonary artery-AAO, MPA, RPA, LPA) of five patients, suffering from severe chronic thromboembolic pulmonary hypertension (CTEPH). Flow maps were evaluated by two independent observers using conventional manual edge detection (INTER m/m). Flow calculations were performed by one observer using both, manual and automated edge detection (INTRA m/a), by a second observer using automated edge detection two times (INTRA a/a) and by two independent observers using automated edge detection (INTER a/a). Evaluation time was measured. Linear regression analysis and Student's t-test were performed.

RESULTS

Overall regression coefficients (r2) for INTER m/m, INTRA m/a, INTER a/a and INTRA a/a, respectively, were as follows: CSA, 0.91, 0.91, 0.96, 0.98; MSV, 0.97, 0.99, 0.99, 0.99; IF, 0.98, 0.99, 0.99, 0.99; F, 0.98, 0.99, 0.99, 0.99. Manual CSA values differed significantly from automated data in MPA (P=0.01), RPA (P=0.0008) and LPA (P=0.02). No difference was found for the other assessed parameters of the pulmonary circulation. Average evaluation time per vessel was 20.2+/-2.6 min for manual and 2.1+/-0.7 min for automated edge detection (P<0.00001).

CONCLUSION

The software program used provided reproducible data, lead to a 90% reduction in evaluation and calculation time and, therefore, might excel the utilization of VEC-MR flow measurements. Despite variations in the evaluation of the pulmonary circulation CSAs, flow assessment is feasible in critically ill patients.

Vessel surface reconstruction with a tubular deformable model

Three-dimensional (3-D) angiographic methods are gaining acceptance for evaluation of atherosclerotic disease. However, measurement of vessel stenosis from 3-D angiographic methods can be problematic due to limited image resolution and contrast. We present a method for reconstructing vessel surfaces from 3-D angiographic methods that allows for objective measurement of vessel stenosis. The method is a deformable model that employs a tubular coordinate system. Vertex merging is incorporated into the coordinate system to maintain even vertex spacing and to avoid problems of self-intersection of the surface. The deformable model was evaluated on clinical magnetic resonance (MR) images of the carotid (n = 6) and renal (n = 2) arteries, on an MR image of a physical vascular phantom and on a digital vascular phantom. Only one gross error occurred for all clinical images. All reconstructed surfaces had a realistic, smooth appearance. For all segments of the physical vascular phantom, vessel radii from the surface reconstruction had an error of less than 0.2 of the average voxel dimension. Variability of manual initialization of the deformable model had negligible effect on the measurement of the degree of stenosis of the digital vascular phantom.

Prostate boundary segmentation from 2D ultrasound images

Outlining, or segmenting, the prostate is a very important task in the assignment of appropriate therapy and dose for cancer treatment; however, manual outlining is tedious and time-consuming. In this paper, an algorithm is described for semiautomatic segmentation of the prostate from 2D ultrasound images. The algorithm uses model-based initialization and the efficient discrete dynamic contour. Initialization requires the user to select only four points from which the outline of the prostate is estimated using cubic interpolation functions and shape information. The estimated contour is then deformed automatically to better fit the image. The algorithm can easily segment a wide range of prostate images, and contour editing tools are included to handle more difficult cases. The performance of the algorithm with a single user was compared to manual outlining by a single expert observer. The average distance between semiautomatically and manually outlined boundaries was found to be less than 5 pixels (0.63 mm), and the accuracy and sensitivity to area measurements were both over 90%.