Laser osteotomy promises precise cutting and minor bone tissue damage. We proposed Optical Coherence Tomography (OCT) to monitor the ablation process toward our smart laser osteotomy approach. The OCT image is helpful to identify tissue type and provide feedback for the ablation laser to avoid critical tissues such as bone marrow and nerve. Furthermore, in the implementation, the tissue classifier's accuracy is dependent on the quality of the OCT image. Therefore, image denoising plays an important role in having an accurate feedback system. A common OCT image denoising technique is the frame-averaging method. Inherent to this method is the need for multiple images, i.e., the more images used, the better the resulting image quality. However, this approach comes at the price of increased acquisition time and sensitivity to motion artifacts. To overcome these limitations, we applied a deep-learning denoising method capable of imitating the frame-averaging method. The resulting image had a similar image quality to the frame-averaging and was better than the classical digital filtering methods. We also evaluated if this method affects the tissue classifier model's accuracy that will provide feedback to the ablation laser. We found that image denoising significantly increased the accuracy of the tissue classifier. Furthermore, we observed that the classifier trained using the deep learning denoised images achieved similar accuracy to the classifier trained using frame-averaged images. The results suggest the possibility of using the deep learning method as a pre-processing step for real-time tissue classification in smart laser osteotomy.

Parametric spatial transformation models have been successfully applied to image registration tasks. In such models, the transformation of interest is parameterized by a fixed set of basis functions as for example B-splines. Each basis function is located on a fixed regular grid position among the image domain because the transformation of interest is not known in advance. As a consequence, not all basis functions will necessarily contribute to the final transformation which results in a non-compact representation of the transformation. We reformulate the pairwise registration problem as a recursive sequence of successive alignments. For each element in the sequence, a local deformation defined by its position, shape, and weight is computed by our recurrent registration neural network. The sum of all lo- cal deformations yield the final spatial alignment of both images. Formulating the registration problem in this way allows the network to detect non-aligned regions in the images and to learn how to locally refine the registration properly. In contrast to current non-sequence-based registration methods, our approach iteratively applies local spatial deformations to the images until the desired registration accuracy is achieved. We trained our network on 2D magnetic resonance images of the lung and compared our method to a standard parametric B-spline registration. The experiments show, that our method performs on par for the accuracy but yields a more compact representation of the transformation. Furthermore, we achieve a speedup of around 15 compared to the B-spline registration.

This paper presents a parallel robotic mechanism for endoscope tip stabilization and guidance for a robot-assisted minimally invasive laser osteotome. The mechanism attaches to the bone of the patient, providing a stable and robust platform for the laser integrated in the endoscope tip which has to be moved precisely in the sub-millimeter range along a preoperatively planned path. This method is only possible because cutting bone with laser instead of using conventional bone drills and saws involves considerably lower interaction forces. The design, kinematics, control, and motion performance of the concept are presented for an upscaled prototype. The obtained deviation of the endoscope tip motion from the reference path lies in the sub-millimeter range. This result allows us to conclude that the concept is more than promising. Furthermore, we expect that the herein presented principle will influence the way osteotomies will be performed in the future.

We present an automatic three-dimensional segmentation approach based on continuous max flow that targets tubular structures in medical images. Our method uses second-order derivative information provided by Frangi et al.’s vesselness feature and exploits it twofold: First, the vesselness response itself is used for localizing the tubular structure of interest. Second, the eigenvectors of the Hessian eigendecomposition guide our anisotropic total variation–regularized segmentation. In a simulation experiment, we demonstrate the superiority of anisotropic as compared to isotropic total variation–regularized segmentation in the presence of noise. In an experiment with magnetic resonance images of the human cervical spinal cord, we compare our automated segmentations to those of two human observers. Finally, a comparison with a dedicated state-of-the-art spinal cord segmentation framework shows that we achieve comparable to superior segmentation quality.

Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a method to learn supporters which are, be it only temporally, useful for determining the position of the object of interest. Our approach exploits the General Hough Transform strategy. It couples the supporters with the target and naturally distinguishes between strongly and weakly coupled motions. By this, the position of an object can be estimated even when it is not seen directly (e.g., fully occluded or outside of the image region) or when it changes its appearance quickly and significantly. Experiments show substantial improvements in model-free tracking as well as in the tracking of “virtual” points, e.g., in medical applications.

The management and publishing of complex presentations is poorly supported by available presentation software. This makes it hard to publish usable and accessible presentation material, and to reuse that material for continuously evolving events. XSLidy provides a XSLT-based approach to generate presentations out of a mix of HTML and structural elements. Using XSLidy, the management and reuse of complex presentations becomes easier, and the results are more user-friendly in terms of usability and accessibility.

An integral part of every augmented reality system is the calibration between camera and camera-mounted tracking markers. Accuracy and robustness of the AR overlay process is greatly influenced by the quality of this step. In order to meet the very high precision requirements of medical skill training applications, we have set up a calibration environment based on direct sensing of LED markers. A simulation framework has been developed to predict and study the achievable accuracy of the backprojection needed for the scene augmentation process. We demonstrate that the simulation is in good agreement with experimental results. Even if a slight improvement of the precision has been observed compared to well-known hand-eye calibration methods, the subpixel accuracy required by our application cannot be achieved even when using commercial tracking systems providing marker positions within very low error limits.