Robotic radical cystectomy - more precision needed?

PURPOSE OF REVIEW: Recently, several trials as well as registry-data analyses investigating the role of robot-assisted radical cystectomy with extra or intracorporal urinary diversion were completed and follow up matured. This review aims to comment on the current evidence-based findings and interpret the future role of the robotic approach as a part of the treatment of bladder cancer. RECENT FINDINGS: Numerous trials and registry-data analyses revealed no significant differences in progression-free and overall survival after open radical cystectomy or robot-assisted radical cystectomy irrespective of urinary diversion. Perioperative parameters, especially intraoperative blood loss, transfusions, thromboembolic events, wound infections and hospitalization were significantly increased in open radical cystectomy. Patients' convalescence, and especially early postsurgical quality of life, was improved by the robotic approach. The highly demanding surgery itself displayed by a flat learning curve required more than 130 surgeries per institution to reach a stable plateau of complications. The performance of high-quality radical cystectomy irrespective of the approach was significantly increased in high-volume centres. Local recurrence occurs in 11% after radical cystectomy. Current research focuses on intraoperatively usable detection methods and instruments to minimize the risk of residual tumour cells. SUMMARY: Taken together, the total intracorporal approach in radical cystectomy holds the potential to improve perioperative parameters and reduces hospitalization without impairing oncological performance of the procedure. To provide best results for the patient radical cystectomy and especially the technically challenging total intracorporal procedure will gain importance in bladder cancer treatment but should be limited to high-volume centres.

Ultra-compact 3D-printed wide-angle cameras realized by multi-aperture freeform optical design.

Simultaneous realization of ultra-large field of view (FOV), large lateral image size, and a small form factor is one of the challenges in imaging lens design and fabrication. All combined this yields an extensive flow of information while conserving ease of integration where space is limited. Here, we present concepts, correction methods and realizations towards freeform multi-aperture wide-angle cameras fabricated by femtosecond direct laser writing (fsDLW). The 3D printing process gives us the design freedom to create 180° × 360° cameras with a flat form factor in the micrometer range by splitting the FOV into several apertures. Highly tilted and decentered non-rotational lens shapes as well as catadioptric elements are used in the optical design to map the FOV onto a flat surface in a Scheimpflug manner. We present methods to measure and correct freeform surfaces with up to 180° surface normals by confocal measurements, and iterative fabrication via fsDLW. Finally, approaches for digital distortion correction and image stitching are demonstrated and two realizations of freeform multi-aperture wide-angle cameras are presented.

In-plane Strain Analysis by Correlating Geometry and Visual Data Through a Gradient-Based Surface Reconstruction.

Abnormalities in tissue can be detected and analyzed by evaluating mechanical properties, such as strain and stiffness. While current sensor systems are effective in measuring longitudinal properties perpendicular to the measurement sensor, identifying in-plane deformation remains a significant challenge. To address this issue, this paper presents a novel method for reconstructing in-plane deformation of observed tissue surfaces using a fringe projection sensor specifically designed for measuring tissue deformations. The method employs the latest techniques from computer vision, such as differentiable rendering, to formulate the in-plane reconstruction as a differentiable optimization problem. This enables the use of gradient-based solvers for an efficient and effective optimization of the problem optimum. Depth information and image information are combined using landmark correspondences between the respective image observations of the undeformed and deformed scenes. By comparing the reconstructed pre- and post-deformation geometry, the in-plane deformation can be revealed through the analysis of relative variations between the corresponding models' geometries. The proposed reconstruction pipeline is validated on an experimental setup, and the potential for intraoperative applications is discussed.

An Enhanced Synthetic Cystoscopic Environment for Use in Monocular Depth Estimation.

As technology advances and sensing devices improve, it is becoming more and more pertinent to ensure accurate positioning of these devices, especially within the human body. This task remains particularly difficult during manual, minimally invasive surgeries such as cystoscopies where only a monocular, endoscopic camera image is available and driven by hand. Tracking relies on optical localization methods, however, existing classical options do not function well in such a dynamic, non-rigid environment. This work builds on recent works using neural networks to learn a supervised depth estimation from synthetically generated images and, in a second training step, use adversarial training to then apply the network on real images. The improvements made to a synthetic cystoscopic environment are done in such a way to reduce the domain gap between the synthetic images and the real ones. Training with the proposed enhanced environment shows distinct improvements over previously published work when applied to real test images.