OBJECTIVE: Stereoelectroencephalography (SEEG) is an established invasive diagnostic technique for use in patients with drug-resistant focal epilepsy evaluated before resective epilepsy surgery. The factors that influence the accuracy of electrode implantation are not fully understood. Adequate accuracy prevents the risk of major surgery complications. Precise knowledge of the anatomical positions of individual electrode contacts is crucial for the interpretation of SEEG recordings and subsequent surgery. METHODS: We developed an image processing pipeline to localize implanted electrodes and detect individual contact positions using computed tomography (CT), as a substitute for time-consuming manual labeling. The algorithm automates measurement of parameters of the electrodes implanted in the skull (bone thickness, implantation angle and depth) for use in modeling of predictive factors that influence implantation accuracy. RESULTS: Fifty-four patients evaluated by SEEG were analyzed. A total of 662 SEEG electrodes with 8,745 contacts were stereotactically inserted. The automated detector localized all contacts with better accuracy than manual labeling (p < 0.001). The retrospective implantation accuracy of the target point was 2.4 ± 1.1 mm. A multifactorial analysis determined that almost 58% of the total error was attributable to measurable factors. The remaining 42% was attributable to random error. CONCLUSION: SEEG contacts can be reliably marked by our proposed method. The trajectory of electrodes can be parametrically analyzed to predict and validate implantation accuracy using a multifactorial model. SIGNIFICANCE: This novel, automated image processing technique is a potentially clinically important, assistive tool for increasing the yield, efficiency, and safety of SEEG.

The estimation of the fabric material property during the folding is presented. The available techniques for the accurate garment folding rely on known material properties. Currently, the properties are estimated by an operator in advance of folding. We propose an iterative strategy, which updates the property while the garment is folded. The estimation is formulated as an optimisation task. It is based on measurements from a laser range finder. The proposed algorithm improves the estimation iteratively and prevents the garment from slipping at the same time. We demonstrate the estimation procedure for 10 fabric strips of different materials.

The ability to perform an accurate robotic fold is essential to obtain the properly folded garment. Available solutions rely on a rough folding surface or on a comprehensive simulation, both preventing the garment from slipping on the table during folding. This paper proposes a new algorithm for a folding path design respecting the garment material properties and preventing the garment slipping. The folding path is derived based on the equilibrium of forces under the simplifying assumptions of a rectangular and homogeneous garment. This approach allows folding the rectangular garment on a low friction table surface as we demonstrated in the experiments performed by a dual-arm robotic testbed.

The work addresses the problem of clothing perception and manipulation by a two armed industrial robot aiming at a real-time automated folding of a piece of garment spread out on a flat surface. A complete solution combining vision sensing, garment segmentation and understanding, planning of the manipulation and its real execution on a robot is proposed. A new polygonal model of a garment is introduced. Fitting the model into a segmented garment contour is used to detect garment landmark points. It is shown how folded variants of the unfolded model can be derived automatically. Universality and usefulness of the model is demonstrated by its favorable performance within the whole folding procedure which is applicable to a variety of garments categories (towel, pants, shirt, etc.) and evaluated experimentally using the two armed robot. The principal novelty with respect to the state of the art is in the new garment polygonal model and its manipulation planning algorithm which leads to the speed up by two orders of magnitude.