Biomedical subjects

Michael R Kaus

Publications and source records attributed to Michael R Kaus.

5 recordsLinked to original sources

PubMed2004

Automated model-based organ delineation for radiotherapy planning in prostatic region.

PURPOSE: Organ delineation is one of the most tedious and time-consuming parts of radiotherapy planning. It is usually performed by manual contouring in two-dimensional slices using simple drawing tools, and it may take several hours to delineate all structures of interest in a three-dimensional (3D) data set used for planning. In this paper, a 3D model-based approach to automated organ delineation is introduced that allows for a significant reduction of the time required for contouring. METHODS AND MATERIALS: The presented method is based on an adaptation of 3D deformable surface models to the boundaries of the anatomic structures of interest. The adaptation is based on a tradeoff between deformations of the model induced by its attraction to certain image features and the shape integrity of the model. To make the concept clinically feasible, interactive tools are introduced that allow quick correction in problematic areas in which the automated model adaptation may fail. A feasibility study with 40 clinical data sets was done for the male pelvic area, in which the risk organs (bladder, rectum, and femoral heads) were segmented by automatically adapting the corresponding organ models. RESULTS: In several cases of the validation study, minor user interaction was required. Nevertheless, a statistically significant reduction in the time required compared with manual organ contouring was achieved. The results of the validation study showed that the presented model-based approach is accurate (1.0-1.7 mm mean error) for the tested anatomic structures. CONCLUSION: A framework for organ delineation in radiotherapy planning is presented, including automated 3D model-based segmentation, as well as tools for interactive corrections. We demonstrated that the proposed approach is significantly more efficient than manual contouring in two-dimensional slices.

Algorithms↗

PubMed2004

Automated segmentation of the left ventricle in cardiac MRI.

We present a fully automated deformable model technique for myocardium segmentation in 3D MRI. Loss of signal due to blood flow, partial volume effects and significant variation of surface grey value appearance make this a difficult problem. We integrate various sources of prior knowledge learned from annotated image data into a deformable model. Inter-individual shape variation is represented by a statistical point distribution model, and the spatial relationship of the epi- and endocardium is modeled by adapting two coupled triangular surface meshes. To robustly accommodate variation of grey value appearance around the myocardiac surface, a prior parametric spatially varying feature model is established by classification of grey value surface profiles. Quantitative validation of 121 3D MRI datasets in end-diastolic (end-systolic) phase demonstrates accuracy and robustness, with 2.45 mm (2.84 mm) mean deviation from manual segmentation.

Algorithms↗

PubMed2004

Statistical validation of image segmentation quality based on a spatial overlap index.

RATIONALE AND OBJECTIVES: To examine a statistical validation method based on the spatial overlap between two sets of segmentations of the same anatomy. MATERIALS AND METHODS: The Dice similarity coefficient (DSC) was used as a statistical validation metric to evaluate the performance of both the reproducibility of manual segmentations and the spatial overlap accuracy of automated probabilistic fractional segmentation of MR images, illustrated on two clinical examples. Example 1: 10 consecutive cases of prostate brachytherapy patients underwent both preoperative 1.5T and intraoperative 0.5T MR imaging. For each case, 5 repeated manual segmentations of the prostate peripheral zone were performed separately on preoperative and on intraoperative images. Example 2: A semi-automated probabilistic fractional segmentation algorithm was applied to MR imaging of 9 cases with 3 types of brain tumors. DSC values were computed and logit-transformed values were compared in the mean with the analysis of variance (ANOVA). RESULTS: Example 1: The mean DSCs of 0.883 (range, 0.876-0.893) with 1.5T preoperative MRI and 0.838 (range, 0.819-0.852) with 0.5T intraoperative MRI (P < .001) were within and at the margin of the range of good reproducibility, respectively. Example 2: Wide ranges of DSC were observed in brain tumor segmentations: Meningiomas (0.519-0.893), astrocytomas (0.487-0.972), and other mixed gliomas (0.490-0.899). CONCLUSION: The DSC value is a simple and useful summary measure of spatial overlap, which can be applied to studies of reproducibility and accuracy in image segmentation. We observed generally satisfactory but variable validation results in two clinical applications. This metric may be adapted for similar validation tasks.

Analysis of Variance↗

PubMed2003

Statistical validation based on parametric receiver operating characteristic analysis of continuous classification data.

RATIONALE AND OBJECTIVES: The accuracy of diagnostic test and imaging segmentation is important in clinical practice because it has a direct impact on therapeutic planning. Statistical validations of classification accuracy was conducted based on parametric receiver operating characteristic analysis, illustrated on three radiologic examples, MATERIALS AND METHODS: Two parametric models were developed for diagnostic or imaging data. Example 1: A semi-automated fractional segmentation algorithm was applied to magnetic resonance imaging of nine cases of brain tumors. The tumor and background pixel data were assumed to have bi-beta distributions. Fractional segmentation was validated against an estimated composite pixel-wise gold standard based on multi-reader manual segmentations. Example 2: The predictive value of 100 cases of spiral computed tomography of ureteral stone sizes, distributed as bi-normal after a non-linear transformation, under two treatment options received. Example 3: One hundred eighty cases had prostate-specific antigen levels measured in a prospective clinical trial. Radical prostatectomy was performed in all to provide a binary gold standard of local and advanced cancer stages. Prostate-specific antigen level was transformed and modeled by bi-normal distributions. In all examples, areas under the receiver operating characteristic curves were computed. RESULTS. The areas under the receiver operating characteristic curves were: Example 1: Fractional segmentation of magnetic resonance imaging of brain tumors: meningiomas (0.924-0.984); astrocytomas (0.786-0.986); and other low-grade gliomas (0.896-0.983). Example 3: Ureteral stone size for treatment planning (0.813). Example 2: Prostate-specific antigen for staging prostate cancer (0.768). CONCLUSION: All clinical examples yielded fair to excellent accuracy. The validation metric area under the receiver operating characteristic curves may be generalized to evaluating the performances of several continuous classifiers related to imaging.

Algorithms↗

PubMed2003

Automated 3-D PDM construction from segmented images using deformable models.

In recent years, several methods have been proposed for constructing statistical shape models to aid image analysis tasks by providing a priori knowledge. Examples include principal component analysis of manually or semiautomatically placed corresponding landmarks on the learning shapes [point distribution models (PDMs)], which is time consuming and subjective. However, automatically establishing surface correspondences continues to be a difficult problem. This paper presents a novel method for the automated construction of three-dimensional PDM from segmented images. Corresponding surface landmarks are established by adapting a triangulated learning shape to segmented volumetric images of the remaining shapes. The adaptation is based on a novel deformable model technique. We illustrate our approach using computed tomography data of the vertebra and the femur. We demonstrate that our method accurately represents and predicts shapes.

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