Frans M. Vos

A model based method for retinal blood vessel detection

Retinal blood vessels are important structures in ophthalmological images. Many detection methods are available, but the results are not always satisfactory. In this paper, we present a novel model based method for blood vessel detection in retinal images. It is based on a Laplace and thresholding segmentation step, followed by a classification step to improve performance. The last step assures incorporation of the inner part of large vessels with specular reflection. The method gives a sensitivity of 92% with a specificity of 91%. The method can be optimized for the specific properties of the blood vessels in the image and it allows for detection of vessels that appear to be split due to specular reflection.

WHITE MATTER FRACTIONAL ANISOTROPY CORRELATES WITH SPEED OF PROCESSING AND MOTOR SPEED IN YOUNG CHILDHOOD CANCER SURVIVORS

PURPOSE To determine whether childhood medulloblastoma and acute lymphoblastic leukemia (ALL) survivors have decreased white matter fractional anisotropy (WMFA) and whether WMFA is related to the speed of processing and motor speed. METHODS AND MATERIALS For this study, 17 patients (6 medulloblastoma, 5 ALL treated with high-dose methotrexate (MTX) (4 x 5 g/m(2)) and 6 with low-dose MTX (3 x 2 g/m(2))) and 17 age-matched controls participated. On a 3.0-T magnetic resonance imaging (MRI) scanner, diffusion tensor imaging (DTI) was performed, and WMFA values were calculated, including specific regions of interest (ROIs), and correlated with the speed of processing and motor speed. RESULTS Mean WMFA in the patient group, mean age 14 years (range 8.9 - 16.9), was decreased compared with the control group (p = 0.01), as well as WMFA in the right inferior fronto-occipital fasciliculus (IFO) (p = 0.03) and in the genu of the corpus callosum (gCC) (p = 0.01). Based on neurocognitive results, significant positive correlations were present between processing speed and WMFA in the splenium (sCC) (r = 0.53, p = 0.03) and the body of the corpus callosum (bCC) (r = 0.52, p = 0.03), whereas the right IFO WMFA was related to motor speed (r = 0.49, p < 0.05). CONCLUSIONS White matter tracts, using a 3.0-T MRI scanner, show impairment in childhood cancer survivors, medulloblastoma survivors, and also those treated with high doses of MTX. In particular, white matter tracts in the sCC, bCC and right IFO are positively correlated with speed of processing and motor speed.

Improving alignment in Tract-based spatial statistics: Evaluation and optimization of image registration

Anatomical alignment in neuroimaging studies is of such importance that considerable effort is put into improving the registration used to establish spatial correspondence. Tract-based spatial statistics (TBSS) is a popular method for comparing diffusion characteristics across subjects. TBSS establishes spatial correspondence using a combination of nonlinear registration and a “skeleton projection” that may break topological consistency of the transformed brain images. We therefore investigated feasibility of replacing the two-stage registration-projection procedure in TBSS with a single, regularized, high-dimensional registration. To optimize registration parameters and to evaluate registration performance in diffusion MRI, we designed an evaluation framework that uses native space probabilistic tractography for 23 white matter tracts, and quantifies tract similarity across subjects in standard space. We optimized parameters for two registration algorithms on two diffusion datasets of different quality. We investigated reproducibility of the evaluation framework, and of the optimized registration algorithms. Next, we compared registration performance of the regularized registration methods and TBSS. Finally, feasibility and effect of incorporating the improved registration in TBSS were evaluated in an example study. The evaluation framework was highly reproducible for both algorithms (R2 0.993; 0.931). The optimal registration parameters depended on the quality of the dataset in a graded and predictable manner. At optimal parameters, both algorithms outperformed the registration of TBSS, showing feasibility of adopting such approaches in TBSS. This was further confirmed in the example experiment.

Computer-Aided Detection of Polyps in CT Colonography Using Logistic Regression

We present a computer-aided detection (CAD) system for computed tomography colonography that orders the polyps according to clinical relevance. The CAD system consists of two steps: candidate detection and supervised classification. The characteristics of the detection step lead to specific choices for the classification system. The candidates are ordered by a linear logistic classifier (logistic regression) based on only three features: the protrusion of the colon wall, the mean internal intensity, and a feature to discard detections on the rectal enema tube. This classifier can cope with a small number of polyps available for training, a large imbalance between polyps and non-polyp candidates, a truncated feature space, unbalanced and unknown misclassification costs, and an exponential distribution with respect to candidate size in feature space. Our CAD system was evaluated with data sets from four different medical centers. For polyps larger than or equal to 6 mm we achieved sensitivities of respectively 95%, 85%, 85%, and 100% with 5, 4, 5, and 6 false positives per scan over 86, 48, 141, and 32 patients. A cross-center evaluation in which the system is trained and tested with data from different sources showed that the trained CAD system generalizes to data from different medical centers and with different patient preparations. This is essential to application in large-scale screening for colorectal polyps.

Grading Crohn disease activity with MRI: interobserver variability of MRI features, MRI scoring of severity, and correlation with Crohn disease endoscopic index of severity.

OBJECTIVE The purpose of this article is to assess the interobserver variability for scoring MRI features of Crohn disease activity and to correlate two MRI scoring systems to the Crohn disease endoscopic index of severity (CDEIS). MATERIALS AND METHODS Thirty-three consecutive patients with Crohn disease undergoing 3-T MRI examinations (T1-weighted with IV contrast medium administration and T2-weighted sequences) and ileocolonoscopy within 1 month were independently evaluated by four readers. Seventeen MRI features were recorded in 143 bowel segments and were used to calculate the MR index of activity and the Crohn disease MRI index (CDMI) score. Multirater analysis was performed for all features and scoring systems using intraclass correlation coefficient (icc) and kappa statistic. Scoring systems were compared with ileocolonoscopy with CDEIS using Spearman rank correlation. RESULTS Thirty patients (median age, 32 years; 21 women and nine men) were included. MRI features showed fair-to-good interobserver variability (intraclass correlation coefficient or kappa varied from 0.30 to 0.69). Wall thickness in millimeters, presence of edema, enhancement pattern, and length of the disease in each segment showed a good interobserver variability between all readers (icc = 0.69, κ = 0.66, κ = 0.62, and κ = 0.62, respectively). The MR index of activity and CDMI scores showed good reproducibility (icc = 0.74 and icc = 0.78, respectively) and moderate CDEIS correlation (r = 0.51 and r = 0.59, respectively). CONCLUSION The reproducibility of individual MRI features overall is fair to good, with good reproducibility for the most commonly used features. When combined into the MR index of activity and CDMI score, overall reproducibility is good. Both scores show moderate agreement with CDEIS.

Computed Cleansing for Virtual Colonoscopy Using a Three-Material Transition Model

Virtual colonoscopy is a non-invasive technique for the detection of polyps. Currently, a clean colon is required; as without cleansing the colonic wall cannot be segmented. Enhanced bowel preparation schemes opacify intraluminal remains to enable colon segmentation. Computed cleansing (as opposed to physical cleansing of the bowels) allows removal of tagged intraluminal remains. This paper describes a model that allows proper classification of transitions between three materials: gas, tissue and tagged intraluminal remains. The computed cleansing effectively detects and removes the remains from the data. Inspection of the ‘clean’ wall is possible using common surface visualization techniques.

Detection and Segmentation of Colonic Polyps on Implicit Isosurfaces by Second Principal Curvature Flow

Todays computer aided detection systems for computed tomography colonography (CTC) enable automated detection and segmentation of colorectal polyps. We present a paradigm shift by proposing a method that measures the amount of protrudedness of a candidate object in a scale adaptive fashion. One of the main results is that the performance of the candidate detection depends only on one parameter, the amount of protrusion. Additionally the method yields correct polyp segmentation without the need of an additional segmentation step. The supervised pattern recognition involves a clear distinction between size related features and features related to shape or intensity. A Mahalanobis transformation of the latter facilitates ranking of the objects using a logistic classifier. We evaluate two implementations of the method on 84 patients with a total of 57 polyps larger than or equal to 6 mm. We obtained a performance of 95% sensitivity at four false positives per scan for polyps larger than or equal to 6 mm.

Fast and reproducible fiber bundle selection in DTI visualization

Diffusion tensor imaging (DTI) is an MRI-based technique for quantifying water diffusion in living tissue. In the white matter of the brain, water diffuses more rapidly along the neuronal axons than in the perpendicular direction. By exploiting this phenomenon, DTI can be used to determine trajectories of fiber bundles, or neuronal connections between regions, in the brain. The resulting bundles can be visualized. However, the resulting visualizations can be complex and difficult to interpret. An effective approach is to pre-determine trajectories from a large number of positions throughout the white matter (full brain fiber tracking) and to offer facilities to aid the user in selecting fiber bundles of interest. Two factors are crucial for the use and acceptance of this technique in clinical studies: firstly, the selection of the bundles by brain experts should be interactive, supported by real-time visualization of the trajectories registered with anatomical MRI scans. Secondly, the fiber selections should be reproducible, so that different experts will achieve the same results. In this paper we present a practical technique for the interactive selection of fiber-bundles using multiple convex objects that is an order of magnitude faster than similar techniques published earlier. We also present the results of a clinical study with ten subjects that show that our selection approach is highly reproducible for fractional anisotropy (FA) calculated over the selected fiber bundles.

Lines of Curvature for Polyp Detection in Virtual Colonoscopy

Computer-aided diagnosis (CAD) is a helpful addition to laborious visual inspection for preselection of suspected colonic polyps in virtual colonoscopy. Most of the previous work on automatic polyp detection makes use of indicators based on the scalar curvature of the colon wall and can result in many false-positive detections. Our work tries to reduce the number of false-positive detections in the preselection of polyp candidates. Polyp surface shape can be characterized and visualized using lines of curvature. In this paper, we describe techniques for generating and rendering lines of curvature on surfaces and we show that these lines can be used as part of a polyp detection approach. We have adapted existing approaches on explicit triangular surface meshes, and developed a new algorithm on implicit surfaces embedded in 3D volume data. The visualization of shaded colonic surfaces can be enhanced by rendering the derived lines of curvature on these surfaces. Features strongly correlated with true-positive detections were calculated on lines of curvature and used for the polyp candidate selection. We studied the performance of these features on 5 data sets that included 331 pre-detected candidates, of which 50 sites were true polyps. The winding angle had a significant discriminating power for true-positive detections, which was demonstrated by a Wilcoxon rank sum test with p<0.001. The median winding angle and inter-quartile range (IQR) for true polyps were 7.817 and 6.770-9.288 compared to 2.954 and 1.995-3.749 for false-positive detections

Statistical descriptions of scaphoid and lunate bone shapes

Diagnosing of injuries of the wrist bones is problematic due to a highly complex and variable geometry. knowledge of variations of healthy bone shapes is essential to detect wrist pathologies, developing prosthetics and investigating biomechanical properties of the wrist joint. In previous literature various methods have been proposed to classify different scaphoid and lunate types. These classifications were mainly qualitative or were based on a limited number of manually determined surface points. The purposes of this study are to develop a quantitative, standardized description of the variations in the scaphoid and lunate and to investigate whether it is feasible to divide carpal bones in isolated shape categories based on statistical grounds. The shape variations of the scaphoid and lunate were described by constructing a statistical shape model (SSM) of healthy bones. SSM shape parameters were determined that describe the deviation of each shape from the mean shape. The first five modes of variation in the SSMs describe 60% of the total variance of the scaphoid and 57% of the lunate. Higher modes describe less than 5% of the variance per mode. The distributions of the parameters that characterize the bone shape variations along the modes do not significantly differ from a normal distribution. The SSM provides a description of possible shape variations and the distribution of scaphoid and lunate shapes in our population at an accuracy of approximately the voxel size (0.3x0.3x0.3mm(3)). The developed statistical shape model represents the previously qualitatively described variations of scaphoid and lunate. However, strict classifications based on shape differences are not feasible on statistical grounds.