In mini-invasive surgery, the laparoscopic ultra-sound probe is visible in the laparoscopic image. We address the problem of estimating the probe pose with respect to the laparoscope without using markers and additional sensors. We propose the first method using a single standard laparoscopic monocular RGB image. It is robust, initialization-free and runs at 10 fps, thus forming a promising tool to improve robotic and augmented reality-based surgery.

Dynamic movement primitives (DMPs) provide an effective method of learning manipulation skills from human demonstration. DMPs can be especially useful for imitating industrial manipulation tasks which are performed by humans and are difficult to model, for instance, deformable object manipulation. In this work the effectiveness of a conventional Cartesian space DMP is enhanced using a compact and efficient representation of dual quaternions (DQ). We demonstrate that our DQ based DMP learning approach that utilizes the geometrical meaning of screw-based kinematics, outperforms traditional decoupled task-space DMPs in terms of accuracy during learning in certain situations. Our DMP formulation affords two additional applications: (1) Filter the noisy and irregular sensing of human demonstration; (2) Limit the robotic manipulator's task-space velocity during teleoperation, thus improving the safety of the robot and the environment. The learning and filtering strategies are validated on a bimanual robotic system and a motion capture system. We demonstrate the effectiveness of DMP based manipulation of deformable object by learning a bimanual deformation trajectory and then using it to perform the same task in new scenarios.

Micro-scale robots introduce great prospective into many different medical applications such as targeted drug delivery, minimally invasive surgery and localized bio-metric diagnostics. This research presents a method for object detection and tracking system of a chain-like magnetic microsphere robots using ultrasound imaging in an in-vitro environment. The method estimates the position of the microrobot in real-time using deep learning techniques. The experiments showed that a spherical microrobot with about 500 m in diameter can be detected and tracked in real-time with a high accuracy in dynamic environments. The results exhibit a high detection and tracking accuracy for one, two and three sphere microrobots with the highest accuracy in detection and tracking around 95 % and 93% respectively.

Ultrasound-Guided Regional Anesthesia (UGRA) becomes a standard procedure in surgical operations and contributes to pain management. It offers the advantages of the targeted nerve detection and provides the visualization of regions of interest such as anatomical structures. However, nerve detection is one of the most challenging tasks that anesthetists can encounter in the UGRA procedure. A computer-aided system that can detect automatically the nerve region would facilitate the anesthetist's daily routine and allow them to concentrate more on the anesthetic delivery. In this paper, we propose a new method based on merging deep learning models from different data to detect the median nerve. The merged architecture consists of two branches, one being one-dimensional (1D) convolutional neural networks (CNN) branch and another 2D CNN branch. The merged architecture aims to learn the high-level features from 1D handcrafted noise-robust features and 2D ultrasound images. The obtained results show the validity and high accuracy of the proposed approach and its robustness.

Texture is an important characteristic for different computer vision tasks and applications. Local binary pattern (LBP) is considered one of the most efficient texture descriptors yet. However, LBP has some notable limitations, in particular its sensitivity to noise. In this paper, we address these criteria by introducing a novel texture descriptor, robust adaptive median binary pattern (RAMBP). RAMBP is based on a process involving classification of noisy pixels, adaptive analysis window, scale analysis, and a comparison of image medians. The proposed method handles images with highly noisy textures and increases the discriminative properties by capturing microstructure and macrostructure texture information. The method was evaluated on popular texture datasets for classification and retrieval tasks and under different high noise conditions. Without any training or prior knowledge of the noise type, RAMBP achieved the best classification compared to state-of-the-art techniques. It scored more than 90% under 50% impulse noise densities, more than 95% under Gaussian noised textures with a standard deviation σ = 5 , more than 99% under Gaussian blurred textures with a standard deviation σ = 1.25 , and more than 90% for mixed noise. The proposed method yielded competitive results and proved to be one of the best descriptors in noise-free texture classification. Furthermore, RAMBP showed high performance for the problem of noisy texture retrieval providing high scores of recall and precision measures for textures with high noise levels. Finally, compared with the state-of-the-art methods, RAMBP achieves a good running time with low feature dimensionality.