10 research outputs found
Automated pick-up of suturing needles for robotic surgical assistance
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate
cancer that involves complete or nerve sparing removal prostate tissue that
contains cancer. After removal the bladder neck is successively sutured
directly with the urethra. The procedure is called urethrovesical anastomosis
and is one of the most dexterity demanding tasks during RALP. Two suturing
instruments and a pair of needles are used in combination to perform a running
stitch during urethrovesical anastomosis. While robotic instruments provide
enhanced dexterity to perform the anastomosis, it is still highly challenging
and difficult to learn. In this paper, we presents a vision-guided needle
grasping method for automatically grasping the needle that has been inserted
into the patient prior to anastomosis. We aim to automatically grasp the
suturing needle in a position that avoids hand-offs and immediately enables the
start of suturing. The full grasping process can be broken down into: a needle
detection algorithm; an approach phase where the surgical tool moves closer to
the needle based on visual feedback; and a grasping phase through path planning
based on observed surgical practice. Our experimental results show examples of
successful autonomous grasping that has the potential to simplify and decrease
the operational time in RALP by assisting a small component of urethrovesical
anastomosis
Automated pick-up of suturing needles for robotic surgical assistance
Robot-assisted laparoscopic prostatectomy (RALP) is a treatment for prostate cancer that involves complete or nerve sparing removal prostate tissue that contains cancer. After removal the bladder neck is successively sutured directly with the urethra. The procedure is called urethrovesical anastomosis and is one of the most dexterity demanding tasks during RALP. Two suturing instruments and a pair of needles are used in combination to perform a running stitch during urethrovesical anastomosis. While robotic instruments provide enhanced dexterity to perform the anastomosis, it is still highly challenging and difficult to learn. In this paper, we presents a vision-guided needle grasping method for automatically grasping the needle that has been inserted into the patient prior to anastomosis. We aim to automatically grasp the suturing needle in a position that avoids hand-offs and immediately enables the start of suturing. The full grasping process can be broken down into: a needle detection algorithm; an approach phase where the surgical tool moves closer to the needle based on visual feedback; and a grasping phase through path planning based on observed surgical practice. Our experimental results show examples of successful autonomous grasping that has the potential to simplify and decrease the operational time in RALP by assisting a small component of urethrovesical anastomosis
Computer Vision in the Surgical Operating Room
Background: Multiple types of surgical cameras are used in modern surgical practice and provide a rich visual signal that is used by surgeons to visualize the clinical site and make clinical decisions. This signal can also be used by artificial intelligence (AI) methods to provide support in identifying instruments, structures, or activities both in real-time during procedures and postoperatively for analytics and understanding of surgical processes. Summary: In this paper, we provide a succinct perspective on the use of AI and especially computer vision to power solutions for the surgical operating room (OR). The synergy between data availability and technical advances in computational power and AI methodology has led to rapid developments in the field and promising advances. Key Messages: With the increasing availability of surgical video sources and the convergence of technologiesaround video storage, processing, and understanding, we believe clinical solutions and products leveraging vision are going to become an important component of modern surgical capabilities. However, both technical and clinical challenges remain to be overcome to efficiently make use of vision-based approaches into the clinic
Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit
Robotic-assisted surgery is now well-established in clinical practice and has
become the gold standard clinical treatment option for several clinical
indications. The field of robotic-assisted surgery is expected to grow
substantially in the next decade with a range of new robotic devices emerging
to address unmet clinical needs across different specialities. A vibrant
surgical robotics research community is pivotal for conceptualizing such new
systems as well as for developing and training the engineers and scientists to
translate them into practice. The da Vinci Research Kit (dVRK), an academic and
industry collaborative effort to re-purpose decommissioned da Vinci surgical
systems (Intuitive Surgical Inc, CA, USA) as a research platform for surgical
robotics research, has been a key initiative for addressing a barrier to entry
for new research groups in surgical robotics. In this paper, we present an
extensive review of the publications that have been facilitated by the dVRK
over the past decade. We classify research efforts into different categories
and outline some of the major challenges and needs for the robotics community
to maintain this initiative and build upon it
Robot Assisted Object Manipulation for Minimally Invasive Surgery
Robotic systems have an increasingly important role in facilitating minimally invasive surgical treatments. In robot-assisted minimally invasive surgery, surgeons remotely control instruments from a console to perform operations inside the patient. However, despite the advanced technological status of surgical robots, fully autonomous systems, with decision-making capabilities, are not yet available.
In 2017, a structure to classify the research efforts toward autonomy achievable with surgical robots was proposed by Yang et al. Six different levels were identified: no autonomy, robot assistance, task autonomy,
conditional autonomy, high autonomy, and full autonomy. All the commercially available platforms in robot-assisted
surgery is still in level 0 (no autonomy). Despite increasing the level of autonomy remains an open challenge, its adoption could potentially introduce multiple benefits, such as decreasing surgeons’ workload and fatigue and pursuing a consistent
quality of procedures. Ultimately, allowing the surgeons to interpret the ample
and intelligent information from the system will enhance the surgical outcome and
positively reflect both on patients and society. Three main aspects are required to
introduce automation into surgery: the surgical robot must move with high precision,
have motion planning capabilities and understand the surgical scene. Besides
these main factors, depending on the type of surgery, there could be other aspects
that might play a fundamental role, to name some compliance, stiffness, etc. This
thesis addresses three technological challenges encountered when trying to achieve
the aforementioned goals, in the specific case of robot-object interaction. First,
how to overcome the inaccuracy of cable-driven systems when executing fine and
precise movements. Second, planning different tasks in dynamically changing environments.
Lastly, how the understanding of a surgical scene can be used to solve
more than one manipulation task.
To address the first challenge, a control scheme relying on accurate calibration is
implemented to execute the pick-up of a surgical needle. Regarding the planning of
surgical tasks, two approaches are explored: one is learning from demonstration to
pick and place a surgical object, and the second is using a gradient-based approach
to trigger a smoother object repositioning phase during intraoperative procedures.
Finally, to improve scene understanding, this thesis focuses on developing a simulation
environment where multiple tasks can be learned based on the surgical scene
and then transferred to the real robot. Experiments proved that automation of the pick and place task of different surgical objects is possible. The robot was successfully
able to autonomously pick up a suturing needle, position a surgical device for
intraoperative ultrasound scanning and manipulate soft tissue for intraoperative organ
retraction. Despite automation of surgical subtasks has been demonstrated in
this work, several challenges remain open, such as the capabilities of the generated
algorithm to generalise over different environment conditions and different patients
Visual Tracking in Robotic Minimally Invasive Surgery
Intra-operative imaging and robotics are some of the technologies driving forward better and more effective minimally invasive surgical procedures. To advance surgical practice and capabilities further, one of the key requirements for computationally enhanced interventions is to know how instruments and tissues move during the operation. While endoscopic video captures motion, the complex appearance dynamic effects of surgical scenes are challenging for computer vision algorithms to handle with robustness. Tackling both tissue and instrument motion estimation, this thesis proposes a combined non-rigid surface deformation estimation method to track tissue surfaces robustly and in conditions with poor illumination. For instrument tracking, a keypoint based 2D tracker that relies on the Generalized Hough Transform is developed to initialize a 3D tracker in order to robustly track surgical instruments through long sequences that contain complex motions. To handle appearance changes and occlusion a patch-based adaptive weighting with segmentation and scale tracking framework is developed. It takes a tracking-by-detection approach and a segmentation model is used to assigns weights to template patches in order to suppress back- ground information. The performance of the method is thoroughly evaluated showing that without any offline-training, the tracker works well even in complex environments. Finally, the thesis proposes a novel 2D articulated instrument pose estimation framework, which includes detection-regression fully convolutional network and a multiple instrument parsing component. The framework achieves compelling performance and illustrates interesting properties includ- ing transfer between different instrument types and between ex vivo and in vivo data. In summary, the thesis advances the state-of-the art in visual tracking for surgical applications for both tissue and instrument motion estimation. It contributes to developing the technological capability of full surgical scene understanding from endoscopic video