Towards Autonomous Robotic Biopsy-Design, Modeling and Control of a Robot for Needle Insertion of a Commercial Full Core Biopsy Instrument.

This paper presents the design, control, and experimental evaluation of a novel fully automated robotic-assisted system for the positioning and insertion of a commercial full core biopsy instrument under guidance by ultrasound imaging. The robotic system consisted of a novel 4 Degree of freedom (DOF) add-on robot for the positioning and insertion of the biopsy instrument that is attached to a UR5-based teleoperation system with 6 DOF. The robotic system incorporates the advantages of both freehand and probe-guided biopsy techniques. The proposed robotic system can be used as a slave robot in a teleoperation configuration or as an autonomous or semi-autonomous robot in the future. While the UR5 manipulator was controlled using a teleoperation scheme with force controller, a reinforcement learning based controller using the Deep Deterministic Policy Gradient (DDPG) algorithm was developed for the add-on robotic system. The dexterous workspace analysis of the add-on robotic system demonstrated that the system has a suitable workspace within the US image. Two sets of comprehensive experiments including four experiments were performed to evaluate the robotic system's performance in terms of the biopsy instrument positioning, and the insertion of the needle inside the ultrasound plane. The experimental results showed the ability of the robotic system for in-plane needle insertion. The overall mean error of all four experiments in the tracking of the needle angle was 0.446°, and the resolution of the needle insertion was 0.002 mm.

Mixed reality - new image technology in experimental use. / Blandet virkelighet ­ ny bildeteknologi i klinisk utprøving.

New methods for holographic visualisation provide a true three-dimensional experience of medical images. The technique is generating great interest among surgeons.

Correction: Dynamic modeling of soft continuum manipulators using lie group variational integration.

[This corrects the article DOI: 10.1371/journal.pone.0236121.].

Dynamic modeling of soft continuum manipulators using lie group variational integration.

This paper presents the derivation and experimental validation of algorithms for modeling and estimation of soft continuum manipulators using Lie group variational integration. Existing approaches are generally limited to static and quasi-static analyses, and are not sufficiently validated for dynamic motion. However, in several applications, models need to consider the dynamical behavior of the continuum manipulators. The proposed modeling and estimation formulation is obtained from a discrete variational principle, and therefore grants outstanding conservation properties to the continuum mechanical model. The main contribution of this article is the experimental validation of the dynamic model of soft continuum manipulators, including external torques and forces (e.g., generated by magnetic fields, friction, and the gravity), by carrying out different experiments with metal rods and polymer-based soft rods. To consider dissipative forces in the validation process, distributed estimation filters are proposed. The experimental and numerical tests also illustrate the algorithm's performance on a magnetically-actuated soft continuum manipulator. The model demonstrates good agreement with dynamic experiments in estimating the tip position of a Polydimethylsiloxane (PDMS) rod. The experimental results show an average absolute error and maximum error in tip position estimation of 0.13 mm and 0.58 mm, respectively, for a manipulator length of 60.55 mm.

Variational based smoke removal in laparoscopic images.

BACKGROUND: In laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces errors for the image processing algorithms (used in image guided surgery), but also reduces the visibility of the observed organs and tissues. To overcome these drawbacks, this work aims to remove smoke in laparoscopic images using an image preprocessing method based on a variational approach. METHODS: In this paper, we present the physical smoke model where the degraded image is separated into two parts: direct attenuation and smoke veil and propose an efficient variational-based desmoking method for laparoscopic images. To estimate the smoke veil, the proposed method relies on the observation that smoke veil has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained smoke veil is then subtracted from the original degraded image, resulting in the direct attenuation part. Finally, the smoke free image is computed using a linear intensity transformation of the direct attenuation part. RESULTS: The performance of the proposed method is evaluated quantitatively and qualitatively using three datasets: two public real smoked laparoscopic datasets and one generated synthetic dataset. No-reference and reduced-reference image quality assessment metrics are used with the two real datasets, and show that the proposed method outperforms the state-of-the-art ones. Besides, standard full-reference ones are employed with the synthetic dataset, and indicate also the good performance of the proposed method. Furthermore, the qualitative visual inspection of the results shows that our method removes smoke effectively from the laparoscopic images. CONCLUSION: All the obtained results show that the proposed approach reduces the smoke effectively while preserving the important perceptual information of the image. This allows to provide a better visualization of the operation field for surgeons and improve the image guided laparoscopic surgery procedure.