A comparative study of breast surface reconstruction for aesthetic outcome assessment

Breast cancer is the most prevalent cancer type in women, and while its survival rate is generally high the aesthetic outcome is an increasingly important factor when evaluating different treatment alternatives. 3D scanning and reconstruction techniques offer a flexible tool for building detailed and accurate 3D breast models that can be used both pre-operatively for surgical planning and post-operatively for aesthetic evaluation. This paper aims at comparing the accuracy of low-cost 3D scanning technologies with the significantly more expensive state-of-the-art 3D commercial scanners in the context of breast 3D reconstruction. We present results from 28 synthetic and clinical RGBD sequences, including 12 unique patients and an anthropomorphic phantom demonstrating the applicability of low-cost RGBD sensors to real clinical cases. Body deformation and homogeneous skin texture pose challenges to the studied reconstruction systems. Although these should be addressed appropriately if higher model quality is warranted, we observe that low-cost sensors are able to obtain valuable reconstructions comparable to the state-of-the-art within an error margin of 3 mm.Comment: This paper has been accepted to MICCAI201

Isotropic reconstruction of 3D fluorescence microscopy images using convolutional neural networks

Fluorescence microscopy images usually show severe anisotropy in axial versus lateral resolution. This hampers downstream processing, i.e. the automatic extraction of quantitative biological data. While deconvolution methods and other techniques to address this problem exist, they are either time consuming to apply or limited in their ability to remove anisotropy. We propose a method to recover isotropic resolution from readily acquired anisotropic data. We achieve this using a convolutional neural network that is trained end-to-end from the same anisotropic body of data we later apply the network to. The network effectively learns to restore the full isotropic resolution by restoring the image under a trained, sample specific image prior. We apply our method to $3$ synthetic and $3$ real datasets and show that our results improve on results from deconvolution and state-of-the-art super-resolution techniques. Finally, we demonstrate that a standard 3D segmentation pipeline performs on the output of our network with comparable accuracy as on the full isotropic data

A preliminary approach to intelligent x-ray imaging for baggage inspection at airports

Identifying explosives in baggage at airports relies on being able to characterize the materials that make up an X-ray image. If a suspicion is generated during the imaging process (step 1), the image data could be enhanced by adapting the scanning parameters (step 2). This paper addresses the first part of this problem and uses textural signatures to recognize and characterize materials and hence enabling system control. Directional Gabor-type filtering was applied to a series of different X-ray images. Images were processed in such a way as to simulate a line scanning geometry. Based on our experiments with images of industrial standards and our own samples it was found that different materials could be characterized in terms of the frequency range and orientation of the filters. It was also found that the signal strength generated by the filters could be used as an indicator of visibility and optimum imaging conditions predicted

Eye Tracker Accuracy: Quantitative Evaluation of the Invisible Eye Center Location

Purpose. We present a new method to evaluate the accuracy of an eye tracker based eye localization system. Measuring the accuracy of an eye tracker's primary intention, the estimated point of gaze, is usually done with volunteers and a set of fixation points used as ground truth. However, verifying the accuracy of the location estimate of a volunteer's eye center in 3D space is not easily possible. This is because the eye center is an intangible point hidden by the iris. Methods. We evaluate the eye location accuracy by using an eye phantom instead of eyes of volunteers. For this, we developed a testing stage with a realistic artificial eye and a corresponding kinematic model, which we trained with {\mu}CT data. This enables us to precisely evaluate the eye location estimate of an eye tracker. Results. We show that the proposed testing stage with the corresponding kinematic model is suitable for such a validation. Further, we evaluate a particular eye tracker based navigation system and show that this system is able to successfully determine the eye center with sub-millimeter accuracy. Conclusions. We show the suitability of the evaluated eye tracker for eye interventions, using the proposed testing stage and the corresponding kinematic model. The results further enable specific enhancement of the navigation system to potentially get even better results