3D Soft Segmentation and Visualization of Medical Data Based on Nonlinear Diffusion and Distance Functions

Visualization of medical 3D data is a complex problem, since the raw data is often unsuitable for standard techniques like Direct Volume Rendering. Some kind of pre-treatment is necessary, usually segmentation of the structures of interest, which in turn is a difficult task. Most segmentation techniques yield a model without indicating any uncertainty. Visualization then can be misleading, especially if the original data is of poor contrast. We address this dilemma proposing a geometric approach based on distance on image manifolds and an alternative approach based on nonlinear diffusion. An effective algorithm solving Hamilton-Jacobi equations allows for computing a distance function for 2D and 3D manifolds at interactive rates. An efficient implementation of a semi-implicit operator splitting scheme accomplishes interactivity for the diffusion-based strategy. We establish a model which incorporates local information about its reliability and can be visualized with standard techniques. When interpreting the result of the segmentation in a diagnostic setting, this information is of utmost importance.EUROVIS - Eurographics /IEEE VGTC Symposium on Visualizatio

Noninvasive referencing of intraocular tumors for external beam radiation therapy using optical coherence tomography: a proof of concept.

PURPOSE External beam radiation therapy is currently considered the most common treatment modality for intraocular tumors. Localization of the tumor and efficient compensation of tumor misalignment with respect to the radiation beam are crucial. According to the state of the art procedure, localization of the target volume is indirectly performed by the invasive surgical implantation of radiopaque clips or is limited to positioning the head using stereoscopic radiographies. This work represents a proof-of-concept for direct and noninvasive tumor referencing based on anterior eye topography acquired using optical coherence tomography (OCT). METHODS A prototype of a head-mounted device has been developed for automatic monitoring of tumor position and orientation in the isocentric reference frame for LINAC based treatment of intraocular tumors. Noninvasive tumor referencing is performed with six degrees of freedom based on anterior eye topography acquired using OCT and registration of a statistical eye model. The proposed prototype was tested based on enucleated pig eyes and registration accuracy was measured by comparison of the resulting transformation with tilt and torsion angles manually induced using a custom-made test bench. RESULTS Validation based on 12 enucleated pig eyes revealed an overall average registration error of 0.26 ± 0.08° in 87 ± 0.7 ms for tilting and 0.52 ± 0.03° in 94 ± 1.4 ms for torsion. Furthermore, dependency of sampling density on mean registration error was quantitatively assessed. CONCLUSIONS The tumor referencing method presented in combination with the statistical eye model introduced in the past has the potential to enable noninvasive treatment and may improve quality, efficacy, and flexibility of external beam radiotherapy of intraocular tumors

A mechanical eyeball phantom for uveal melanoma radiosurgery by cyberknife

A treatment option for uveal melanoma has been investigated using the Cyberknife system, due to its advantage of real-time image guidance during therapy. However, unpredictable eyeball movement imposes challenges to the state-of-art technology. As a solution, we derived a 2D/3D transformation algorithm that is based on the pupil’s 2D coordinates captured by an optical tracking system to predict the tumor’s 3D positions in real-time. In order to validate our developed algorithm and other methods, a mechanical phantom that can simulate the eyeball’s movement is highly desirable.We designed a mechanical phantom that consists of a camera module, an eyeball module with an embedded “tumor", an eyeball holder module, and an eyeball moving module. All materials are made with acrylic or nylon plastics with the exception of the linear motion stages and the camera.In the calibration procedure, the phantom is scanned using a CT scanner. By using the recorded pupil’s coordinates and extracted tumor coordinates, the 2D/3D transformation model yields 0.39 ± 0.09 mm root-sum-squared error for five calibration positions between the actual 3D coordinates and the predicted coordinates. In the validation procedure, the eyeball is rotated to 11 different positions through the mechanical phantom. The 2D/3D transformation model yields 0.58 ± 0.27 mm root-sum-squared error for these positions between the Cyberknife-identified 3D coordinates and the predicted coordinates. The eyeball’s position can be controlled within millimeter accuracy.The designed mechanical phantom is suitable for validating image-guided radiosurgery methods. Further dynamic evaluations can confirm these methods for clinical applications