Robotic Endoscope Control Via Autonomous Instrument Tracking.

Many keyhole interventions rely on bi-manual handling of surgical instruments, forcing the main surgeon to rely on a second surgeon to act as a camera assistant. In addition to the burden of excessively involving surgical staff, this may lead to reduced image stability, increased task completion time and sometimes errors due to the monotony of the task. Robotic endoscope holders, controlled by a set of basic instructions, have been proposed as an alternative, but their unnatural handling may increase the cognitive load of the (solo) surgeon, which hinders their clinical acceptance. More seamless integration in the surgical workflow would be achieved if robotic endoscope holders collaborated with the operating surgeon via semantically rich instructions that closely resemble instructions that would otherwise be issued to a human camera assistant, such as "focus on my right-hand instrument." As a proof of concept, this paper presents a novel system that paves the way towards a synergistic interaction between surgeons and robotic endoscope holders. The proposed platform allows the surgeon to perform a bimanual coordination and navigation task, while a robotic arm autonomously performs the endoscope positioning tasks. Within our system, we propose a novel tooltip localization method based on surgical tool segmentation and a novel visual servoing approach that ensures smooth and appropriate motion of the endoscope camera. We validate our vision pipeline and run a user study of this system. The clinical relevance of the study is ensured through the use of a laparoscopic exercise validated by the European Academy of Gynaecological Surgery which involves bi-manual coordination and navigation. Successful application of our proposed system provides a promising starting point towards broader clinical adoption of robotic endoscope holders.

Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy.

PURPOSE: Twin-to-twin transfusion syndrome (TTTS) is a placental defect occurring in monochorionic twin pregnancies. It is associated with high risks of fetal loss and perinatal death. Fetoscopic elective laser ablation (ELA) of placental anastomoses has been established as the most effective therapy for TTTS. Current tools and techniques face limitations in case of more complex ELA cases. Visualization of the entire placental surface and vascular equator; maintaining an adequate distance and a close to perpendicular angle between laser fiber and placental surface are central for the effectiveness of laser ablation and procedural success. Robot-assisted technology could address these challenges, offer enhanced dexterity and ultimately improve the safety and effectiveness of the therapeutic procedures. METHODS: This work proposes a 'minimal' robotic TTTS approach whereby rather than deploying a massive and expensive robotic system, a compact instrument is 'robotised' and endowed with 'robotic' skills so that operators can quickly and efficiently use it. The work reports on automatic placental pose estimation in fetoscopic images. This estimator forms a key building block of a proposed shared-control approach for semi-autonomous fetoscopy. A convolutional neural network (CNN) is trained to predict the relative orientation of the placental surface from a single monocular fetoscope camera image. To overcome the absence of real-life ground-truth placenta pose data, similar to other works in literature (Handa et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Gaidon et al. in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2016; Vercauteren et al. in: Proceedings of the IEEE, 2019) the network is trained with data generated in a simulated environment and an in-silico phantom model. A limited set of coarsely manually labeled samples from real interventions are added to the training dataset to improve domain adaptation. RESULTS: The trained network shows promising results on unseen samples from synthetic, phantom and in vivo patient data. The performance of the network for collaborative control purposes was evaluated in a virtual reality simulator in which the virtual flexible distal tip was autonomously controlled by the neural network. CONCLUSION: Improved alignment was established compared to manual operation for this setting, demonstrating the feasibility to incorporate a CNN-based estimator in a real-time shared control scheme for fetoscopic applications.

Deep learning-based fetoscopic mosaicking for field-of-view expansion.

PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure. METHODS: We propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos. RESULTS: We perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods. CONCLUSION: The proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution.

A mixed-reality surgical trainer with comprehensive sensing for fetal laser minimally invasive surgery.

PURPOSE: Smaller incisions and reduced surgical trauma made minimally invasive surgery (MIS) grow in popularity even though long training is required to master the instrument manipulation constraints. While numerous training systems have been developed in the past, very few of them tackled fetal surgery and more specifically the treatment of twin-twin transfusion syndrome (TTTS). To address this lack of training resources, this paper presents a novel mixed-reality surgical trainer equipped with comprehensive sensing for TTTS procedures. The proposed trainer combines the benefits of box trainer technology and virtual reality systems. Face and content validation studies are presented and a use-case highlights the benefits of having embedded sensors. METHODS: Face and content validity of the developed setup was assessed by asking surgeons from the field of fetal MIS to accomplish specific tasks on the trainer. A small use-case investigates whether the trainer sensors are able to distinguish between an easy and difficult scenario. RESULTS: The trainer was deemed sufficiently realistic and its proposed tasks relevant for practicing the required motor skills. The use-case demonstrated that the motion and force sensing capabilities of the trainer were able to analyze surgical skill. CONCLUSION: The developed trainer for fetal laser surgery was validated by surgeons from a specialized center in fetal medicine. Further similar investigations in other centers are of interest, as well as quality improvements which will allow to increase the difficulty of the trainer. The comprehensive sensing appeared to be capable of objectively assessing skill.

Haptic Guidance Based on All-Optical Ultrasound Distance Sensing for Safer Minimally Invasive Fetal Surgery.

By intervening during the early stage of gestation, fetal surgeons aim to correct or minimize the effects of congenital disorders. As compared to postnatal treatment of these disorders, such early interventions can often actually save the life of the fetus and also improve the quality of life of the newborn. However, fetal surgery is considered one of the most challenging disciplines within Minimally Invasive Surgery (MIS), owing to factors such as the fragility of the anatomic features, poor visibility, limited manoeuvrability, and extreme requirements in terms of instrument handling with precise positioning. This work is centered on a fetal laser surgery procedure treating placental disorders. It proposes the use of haptic guidance to enhance the overall safety of this procedure and to simplify instrument handling. A method is described that provides effective guidance by installing a forbidden region virtual fixture over the placenta, thereby safeguarding adequate clearance between the instrument tip and the placenta. With a novel application of all-optical ultrasound distance sensing in which transmission and reception are performed with fibre optics, this method can be used with a sole reliance on intraoperatively acquired data. The added value of the guidance approach, in terms of safety and performance, is demonstrated in a series of experiments with a robotic platform.

From a Disposable Ureteroscope to an Active Lightweight Fetoscope-Characterization and Usability Evaluation.

The twin-to-twin transfusion syndrome is a severe fetal anomaly appearing in up to 15% of identical twin pregnancies. This anomaly occurs when twins share blood vessels from a common placenta. The complication leads to an unbalanced blood transfusion between both fetuses. A current surgical treatment consists in coagulating the shared vessels using a fetoscope with an embedded laser. Such treatment is very delicate and constraining due to limited vision and size of the insertion area. The rigidity and lack of controllability of the current used instruments add an additional difficulty and limit the choice in insertion site. This letter proposes an improved flexible fetoscope, offering an enhanced laser controllability and higher versatility regarding the location of the insertion site. A better approach angle can therefore be realized. Also, tissue damage may be further reduced. This single-handed controllable active fetoscope is obtained after adaptation of a LithoVue (Boston Scientific, Natick, MA, USA), a commercially available passive flexible ureteroscope. The LithoVue is fitted with a unique lightweight add-on actuation module foreseen of an artificial muscle and a dedicated control system. Experiments in a mixed reality trainer suggested that the proposed fetoscope is compact, ergonomic, and intuitive in use, allowing an adequate control of the flexible end.

Estimation of optimal pivot point for remote center of motion alignment in surgery.

PURPOSE: The determination of an optimal pivot point ([Formula: see text]) is important for instrument manipulation in minimally invasive surgery. Such knowledge is of particular importance for robotic-assisted surgery where robots need to rotate precisely around a specific point in space in order to minimize trauma to the body wall while maintaining position control. Remote center of motion (RCM) mechanisms are commonly used, where the RCM point is manually and visually aligned. If not positioned appropriately, this misalignment might lead to intolerably high forces on the body wall with increased risk of postoperative complications or instrument damage. An automated method to align the RCM with the [Formula: see text] was developed and tested. METHOD: Computer vision and a lightweight calibration procedure are used to estimate the optimal pivot point. One or two pre-calibrated cameras viewing the surgical scene are employed. The surgeon is asked to make short pivoting movements, applying as little torque as possible, with an instrument of choice passing through the insertion point while camera images are being recorded. The physical properties of an instrument rotating around a pivot point are exploited in a random sample consensus scheme to robustly estimate the ideal position of the RCM in the image planes. Triangulation is used to estimate the RCM position in 3D. Experiments were performed on a specially designed mockup to test the method. RESULTS: The position of the pivot point is estimated with an average error less than 1.85 mm using two webcams placed from approximately 30 cm to 1 m away from the scene. The entire procedure was completed in a few seconds. CONCLUSION: In automated method to estimate the ideal position of the RCM was shown to be reliable. The method can be implemented within a visual servoing approach to automatically place the RCM point, or the results can be displayed on a screen to provide guidance to the surgeon. Further work includes the development of an image-guided alignment method and validation with in vivo experiments.