Continuum robot actuation by a single motor per antagonistic tendon pair: Workspace and repeatability analysis [Kontinuumsroboter-Aktuierung mittels eines Motors pro antagonistischen Kabelpaar. Arbeitsraum- und Wiederholgenauigkeitsanalyse]

Kontinuumroboter sind stark im Fokus aktueller medizinrobotischer Forschung. Da die meisten der in der Literatur vorgestellten Systeme jedoch komplexe und große Aktoreinheiten aufweisen, kann das Erstellen eines solchen Systems in aufwendigen, kostenintensiven und sperrigen Aufbauten resultieren, welche ungeeignet für die räumlichen Anforderungen des Einsatzes in medizinischen Szenarien sind. In dieser Arbeit wird ein einfaches, effizientes kontinuumrobotisches System vorgestellt, in welchem ein antagonistisches Paar von Kabelzügen durch einen Servomotor bewegt wird, anstatt jedes Kabel durch einen einzelnen Motor zu treiben. Auf diese Weise kann die Grundfläche der Aktoreinheit klein gehalten werden und die Methode resultiert in einem einfacheren Aufbau. Der resultierende 260 mm lange Roboter mit 9,9 mm Durchmesser erreicht eine Wiederholgenauigkeit von 1,8 % seiner Länge. In zukünftigen Arbeiten dient er als Basis für die Integration von verschiedener Sensormodalitäten in Kontinuumroboter und zur Evaluation von Steueralgorithmen

Evaluation of Methods for Semantic Segmentation of Endoscopic Images

We examined multiple semantic segmentation methods, which consider the information contained in endoscopic images at different levels of abstraction in order to predict semantic segmentation masks. These segmentations can be used to obtain position information of surgical instruments in endoscopic images, which is the foundation for many computer assisted systems, such as automatic instrument tracking systems. The methods in this paper were examined and compared in regard to their accuracy, effort to create the data set, and inference time. Of all the investigated approaches, the LinkNet34 encoder-decoder network scored best, achieving an Intersection over Union score of 0.838 with an inference time of 30.25 ms on a 640 x 480 pixel input image with a NVIDIA GTX 1070Ti GPU

Ensemble CNN Networks for GBM Tumors Segmentation Using Multi-parametric MRI

Glioblastomas are the most aggressive fast-growing primary brain cancer which originate in the glial cells of the brain. Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation. The Brain Tumor Segmentation Challenge (BraTS) has been a popular benchmark for automatic brain glioblastomas segmentation algorithms since its initiation. In this year, BraTS 2021 challenge provides the largest multi-parametric (mpMRI) dataset of 2,000 pre-operative patients. In this paper, we propose a new aggregation of two deep learning frameworks namely, DeepSeg and nnU-Net for automatic glioblastoma recognition in pre-operative mpMRI. Our ensemble method obtains Dice similarity scores of 92.00, 87.33, and 84.10 and Hausdorff Distances of 3.81, 8.91, and 16.02 for the enhancing tumor, tumor core, and whole tumor regions, respectively, on the BraTS 2021 validation set, ranking us among the top ten teams. These experimental findings provide evidence that it can be readily applied clinically and thereby aiding in the brain cancer prognosis, therapy planning, and therapy response monitoring. A docker image for reproducing our segmentation results is available online at https://hub.docker.com/r/razeineldin/deepseg21

Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients

Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes. This paper describes our contribution to the Registration of the longitudinal brain MRI task of the Brain Tumor Sequence Registration Challenge 2022 (BraTS-Reg 2022). We developed an enhanced unsupervised learning-based method that extends the iRegNet. In particular, incorporating an unsupervised learning-based paradigm as well as several minor modifications to the network pipeline, allows the enhanced iRegNet method to achieve respectable results. Experimental findings show that the enhanced self-supervised model is able to improve the initial mean median registration absolute error (MAE) from 8.20 (7.62) mm to the lowest value of 3.51 (3.50) for the training set while achieving an MAE of 2.93 (1.63) mm for the validation set. Additional qualitative validation of this study was conducted through overlaying pre-post MRI pairs before and after the de-formable registration. The proposed method scored 5th place during the testing phase of the MICCAI BraTS-Reg 2022 challenge. The docker image to reproduce our BraTS-Reg submission results will be publicly available.Comment: Accepted in the MICCAI BraTS-Reg 2022 Challenge (as part of the BrainLes workshop proceedings distributed by Springer LNCS

Collaborative Control for Surgical Robots

We are designing and evaluating control strategies that enable surgeons to intuitively hand-guide an endoscope attached to a redundant lightweight robot. The strategies focus on safety aspects as well as intuitive and smooth control for moving the endoscope. Two scenarios are addressed. The first being a compliant hand-guidance of the endoscope and the second moving the robot’s elbow on its redundancy circle to move the robot out of the surgeon’s way without changing the view. To prevent collisions with the patient and the environment, the robot needs to move respecting Cartesian constraints

Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object. We thus require a system that can recognize and locate these segments in sensor data of deformed real world objects. This is normally done using deformable object registration, which is problem specific and complex to tune. Recent methods utilize neural occupancy functions to improve deformable object registration by registering to an object reconstruction. Going one step further, we propose a system that in addition to reconstruction learns segmentation of the reconstructed object. As the resulting output already contains the information about the segments, we can skip the registration process. Tested on a variety of deformable objects in simulation and the real world, we demonstrate that our method learns to robustly find these segments. We also introduce a simple sampling algorithm to generate better training data for occupancy learning.Comment: Accepted at WACV 202