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Biomedical subjects

F Maes

Publications and source records attributed to F Maes.

At least 19 recordsLinked to original sources
PubMed2001

Interobserver variations in gross tumor volume delineation of brain tumors on computed tomography and impact of magnetic resonance imaging.

PURPOSE: (1) To assess the interobserver variability of brain tumor delineation on computed tomography (CT). (2) To assess the impact of the addition of magnetic resonance imaging (MRI) information. METHODS: Nine physicians were asked to delineate the gross tumor volume (GTV) of five patients with supratentorial inoperable brain tumors on CT scans and 2 weeks (or more) later on MRIs. The delineations were performed on a computer screen. During delineation on MRI, the registered CT images (without delineation) were displayed on the screen (MRI+CT). RESULTS: A high interobserver variability in GTV delineation on CT is found: the ratio of the largest to the smallest defined volumes varies for the five patients by factors of resp. 2.8, 1.8, 1.8, 1.9 and 1.7. The interobserver variability is as large on MRI+CT as on CT alone (ratio largest/smallest volume: 2.4, 1.7, 1.9, 2.7 and 1.5). Volumes delineated on MRI+CT (mean: 69.6 cm(3)) are larger than on CT alone (mean: 59.5 cm(3)). Residual volumes (volume delineated on one image modality but not on the other) are >0 for CT alone and for MRI+CT. CONCLUSIONS: A large interobserver variability in GTV delineation of brain tumors is demonstrated. The addition of MRI to CT does not reduce interobserver variability. GTVs delineated on MRI+CT are larger than on CT alone, but some volumes are delineated on CT and not on MRI. Therefore, a combination of the two image modalities is recommended for brain tumor delineation for treatment planning.

Brain Neoplasms↗
PubMed2001

Automated segmentation of multiple sclerosis lesions by model outlier detection.

This paper presents a fully automated algorithm for segmentation of multiple sclerosis (MS) lesions from multispectral magnetic resonance (MR) images. The method performs intensity-based tissue classification using a stochastic model for normal brain images and simultaneously detects MS lesions as outliers that are not well explained by the model. It corrects for MR field inhomogeneities, estimates tissue-specific intensity models from the data itself, and incorporates contextual information in the classification using a Markov random field. The results of the automated method are compared with lesion delineations by human experts, showing a high total lesion load correlation. When the degree of spatial correspondence between segmentations is taken into account, considerable disagreement is found, both between expert segmentations, and between expert and automatic measurements.

Algorithms↗
PubMed1999

The contribution of magnetic resonance imaging to the three-dimensional treatment planning of localized prostate cancer.

PURPOSE: To investigate whether the use of transaxial and coronal MR imaging improves the ability to localize the apex of the prostate and the anterior part of the rectum compared to the use of transaxial CT alone, and whether the incorporation of MR could improve the coverage of the prostate by the radiotherapy field and change the volume of rectum irradiated. METHODS AND MATERIALS: Ten consecutive patients with localized prostate carcinoma underwent a CT and an axial and coronal MR scan in treatment position. The CT and MR images were mathematically aligned, and three observers were asked to contour independently the prostate and the rectum on CT and on MR. The interobserver variability of the prostatic apex location and of the delineation of the anterior rectal wall were assessed for each image modality. A dosimetry study was performed to evaluate the dose to the rectum when MR was used in addition to CT to localize the pelvic organs. RESULTS: The interobserver variation of the prostatic apex location was largest on CT ranging from 0.54 to 1.07 cm, and smallest on coronal MR ranging from 0.17 to 0.25 cm. The interobserver variation of the delineation of the anterior rectum on MR was small and constant along the whole length of the prostate (0.09+/-0.02 cm), while for CT it was comparable to that for the MR delineation at the base of the prostate, but it increased gradually towards the apex, where the variation reached 0.39 cm. The volume of MR rectum receiving more than 80% of the prescribed dose was on average reduced by 23.8+/-11.2% from the CT to the MR treatment plan. CONCLUSION: It can be concluded that the additional use of axial and coronal MR scans, in designing the treatment plan for localized prostate carcinoma, improves substantially the localization accuracy of the prostatic apex and the anterior aspect of the rectum, resulting in a better coverage of the prostate and a potential to reduce the volume of the rectum irradiated to a high dose.

Dose-Response Relationship, Radiation↗
PubMed1999

Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information.

Maximization of mutual information of voxel intensities has been demonstrated to be a very powerful criterion for three-dimensional medical image registration, allowing robust and accurate fully automated affine registration of multimodal images in a variety of applications, without the need for segmentation or other preprocessing of the images. In this paper, we investigate the performance of various optimization methods and multiresolution strategies for maximization of mutual information, aiming at increasing registration speed when matching large high-resolution images. We show that mutual information is a continuous function of the affine registration parameters when appropriate interpolation is used and we derive analytic expressions of its derivatives that allow numerically exact evaluation of its gradient. Various multiresolution gradient- and non-gradient-based optimization strategies, such as Powell, simplex, steepest-descent, conjugate-gradient, quasi-Newton and Levenberg-Marquardt methods, are evaluated for registration of computed tomography (CT) and magnetic resonance images of the brain. Speed-ups of a factor of 3 on average compared to Powell's method at full resolution are achieved with similar precision and without a loss of robustness with the simplex, conjugate-gradient and Levenberg-Marquardt method using a two-level multiresolution scheme. Large data sets such as 256(2) x 128 MR and 512(2) x 48 CT images can be registered with subvoxel precision in <5 min CPU time on current workstations.

Algorithms↗
PubMed1999

Automated model-based bias field correction of MR images of the brain.

We propose a model-based method for fully automated bias field correction of MR brain images. The MR signal is modeled as a realization of a random process with a parametric probability distribution that is corrupted by a smooth polynomial inhomogeneity or bias field. The method we propose applies an iterative expectation-maximization (EM) strategy that interleaves pixel classification with estimation of class distribution and bias field parameters, improving the likelihood of the model parameters at each iteration. The algorithm, which can handle multichannel data and slice-by-slice constant intensity offsets, is initialized with information from a digital brain atlas about the a priori expected location of tissue classes. This allows full automation of the method without need for user interaction, yielding more objective and reproducible results. We have validated the bias correction algorithm on simulated data and we illustrate its performance on various MR images with important field inhomogeneities. We also relate the proposed algorithm to other bias correction algorithms.

Algorithms↗
PubMed1999

Automated model-based tissue classification of MR images of the brain.

We describe a fully automated method for model-based tissue classification of magnetic resonance (MR) images of the brain. The method interleaves classification with estimation of the model parameters, improving the classification at each iteration. The algorithm is able to segment single- and multispectral MR images, corrects for MR signal inhomogeneities, and incorporates contextual information by means of Markov random Fields (MRF's). A digital brain atlas containing prior expectations about the spatial location of tissue classes is used to initialize the algorithm. This makes the method fully automated and therefore it provides objective and reproducible segmentations. We have validated the technique on simulated as well as on real MR images of the brain.

Algorithms↗
PubMed1999

Automatic 3-D segmentation of internal structures of the head in MR images using a combination of similarity and free-form transformations: Part I, Methodology and validation on normal subjects.

The study presented in this paper tests the hypothesis that the combination of a global similarity transformation and local free-form deformations can be used for the accurate segmentation of internal structures in MR images of the brain. To quantitatively evaluate our approach, the entire brain, the cerebellum, and the head of the caudate have been segmented manually by two raters on one of the volumes (the reference volume) and mapped back onto all the other volumes, using the computed transformations. The contours so obtained have been compared to contours drawn manually around the structures of interest in each individual brain. Manual delineation was performed twice by the same two raters to test inter- and intrarater variability. For the brain and the cerebellum, results indicate that for each rater, contours obtained manually and contours obtained automatically by deforming his own atlas are virtually indistinguishable. Furthermore, contours obtained manually by one rater and contours obtained automatically by deforming this rater's own atlas are more similar than contours obtained manually by two raters. For the caudate, manual intra- and interrater similarity indexes remain slightly better than manual versus automatic indexes, mainly because of the spatial resolution of the images used in this study. Qualitative results also suggest that this method can be used for the segmentation of more complex structures, such as the hippocampus.

Algorithms↗
PubMed1998

The EASI project--improving the effectiveness and quality of image-guided surgery.

In recent years, advances in computer technology and a significant increase in the accuracy of medical imaging have made it possible to develop systems that can assist the clinician in diagnosis, planning, and treatment. This paper deals with an area that is generally referred to as computer-assisted surgery, image-directed surgery, or image-guided surgery. We report the research, development, and clinical validation performed since January 1996 in the European Applications in Surgical Interventions (EASI) project, which is funded by the European Commission in their "4th Framework Telematics Applications for Health" program. The goal of this project is the improvement of the effectiveness and quality of image-guided neurosurgery of the brain and image-guided vascular surgery of abdominal aortic aneurysms, while at the same time reducing patient risks and overall cost. We have developed advanced prototype systems for preoperative surgical planning and intraoperative surgical navigation, and we have extensively clinically validated these systems. The prototype systems and the clinical validation results are described in this paper.

Quality of Health Care↗
PubMed1997

Evaluation of manual vs semi-automated delineation of liver lesions on CT images.

In this paper we compare a semi-automated delineation method with totally manual delineation for area quantification, with respect to efficiency, quality, and intra- and interobserver variability. Liver lesions on 28 CT images were delineated by three observers, twice using completely manual delineation and twice using a semi-automated method. Quantitative comparisons were performed with respect to delineated area and time required for the delineation tasks. Subjective comparisons were performed with respect to efficiency and perceived quality of the semi-automated method. The areas obtained using semi-automated delineation were significantly smaller (11 %) than those obtained using totally manual delineation. Intraobserver and interobserver variability with the semi-automated method were approximately three times lower than with manual delineation. Efficiency of the semi-automated method was subjectively rated favorable, although further improvements are possible. With respect to quality, the semi-automated method was ranked better than the manual method in 73 % of cases.

Humans↗
PubMed1997

Multimodality image registration by maximization of mutual information.

A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images, which is assumed to be maximal if the images are geometrically aligned. Maximization of MI is a very general and powerful criterion, because no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the MI criterion is validated for rigid body registration of computed tomography (CT), magnetic resonance (MR), and photon emission tomography (PET) images by comparison with the stereotactic registration solution, while robustness is evaluated with respect to implementation issues, such as interpolation and optimization, and image content, including partial overlap and image degradation. Our results demonstrate that subvoxel accuracy with respect to the stereotactic reference solution can be achieved completely automatically and without any prior segmentation, feature extraction, or other preprocessing steps which makes this method very well suited for clinical applications.

Algorithms↗
PubMed1981

Significance of persisting IgM anti-HBc antibodies in hepatitis B virus infection.

Igg and IgM antibodies to the core antigen of hepatitis B virus (HBV) were measured in 136 patients who developed acute HBV hepatitis and who were followed prospectively. After acute hepatitis all the patients developed transiently IgM anti-HBc lasting for two to five months. In contrast, IgM anti-HBc persisted 8 and 9 months in two patients who developed persistent hepatitis and were continuously detected for two years in nine patients who developed aggressive hepatitis. The results suggest that the determination of IgM anti-HBc might be useful to predict the outcome of chronic hepatitis B infection.

Acute Disease↗