Category: News

Workshop 1: Multisensory Transparency-Augmented Teleoperation in Extreme Environments (https://lnkd.in/gcgvKa_h)

Talk Title: Multisensory augmentation and learning of robotic teleoperation in surgical environments

Workshop 2: 2nd Workshop on Machine Learning in Medical Robotics: Bridging ML Theory and Clinical Frontiers (https://lnkd.in/gCK5fVqW)

Talk Title: Compliant surgical motion generation and perception towards intelligent minimally invasive robotic procedures

Additionally, we have two papers on neurosurgical robots and OCT presented at the main conference, including

Paper 1: Head-Mounted Hydraulic Needle Driver for Targeted Interventions in Neurosurgery 

Authors: Zhiwei Fang, Chao (Oliver) Xu, Huxin Gao, Danny Tat-Ming Chan, Prof. Wu ‘Scott’ YUAN, Prof. Hongliang Ren

Paper 2: Towards Electricity-Free Pneumatic Miniature Rotation Actuator for Optical Coherence Tomography (OCT) Endoscopy

Authors: Tinghua Zhang, Sishen YUAN, Chao (Oliver) Xu, Peng Liu, Prof. Hongliang Ren, Prof. Wu ‘Scott’ YUAN

These works are based on strong collaboration between LabREN (http://www.labren.org/mm/) and ABILab (https://lnkd.in/gUuzQqDt). We are looking forward to continuing our collaborative efforts in these areas.

During the 2nd Hong Kong Clinical-Driven Robotics and Embodied AI TEchnology (#CREATE) Symposium held by Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science & Innovation, CAS, our lab lead Prof. Hongliang Ren gave a talk entiled “Compliant Endoscopic Multisensory Guidance With Soft Flexible Robotics”, featuring our recent research progress in multisensory perception for endoluminal soft robotic operation.

A particular highlight was our effort towards automated and intelligent Endoscopic Submucosal Dissection (ESD) surgery, empowered by our indigenous DREAMS (Dual-arm Robotic Endoscopic Assistant for Minimally Invasive Surgery, https://lnkd.in/gQ3PcMsn) platform. We demonstrated how this platform can enhance ESD procedures when equipped with advanced features such as precise trajectory planning, intelligent cutting decision support, accurate reconstruction, and granular analysis and prediction of motion (https://lnkd.in/gkF6A4QY) empowered by Large Visual-Language Models (LVLM).

In addition to our progress in ESD, we also presented other projects, including surgical scene reconstruction & depth estimation (https://lnkd.in/gviyHxAp, https://lnkd.in/gtDwbyWg), augmented reality applications in surgery, the skull-mounted neuro-interventional robot (SkullBot, https://lnkd.in/gn8N9zdU), and OCT for port-wine stain analysis (https://lnkd.in/gm6iH3-m).

This visit not only provided an opportunity for knowledge exchange and collaboration but also reaffirmed our lab’s commitment to pushing the boundaries of innovation in the fields of medical robotics and intelligent surgical technologies. We look forward to furthering these conversations and forging new partnerships that will drive the future of healthcare forward.

On the Info Day for Undergraduate Admissions 2024, we were thrilled to see a big crowd of visitors joining the admission talk and special event “𝐖𝐞𝐥𝐥-𝐑𝐨𝐮𝐧𝐝𝐞𝐝 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫 𝐈𝐧𝐜𝐮𝐛𝐚𝐭𝐢𝐨𝐧 𝐒𝐡𝐨𝐰𝐫𝐨𝐨𝐦” to learn the experience in developing APP / Products / Services.

Our lab member, Sishen YUAN, along with others, showcased our progress in augmented reality for neuro-interventional head-mounted robotics (SkullBot), attracting a lot of interest from the visitors and sparking engaging conversations about the future of surgical technology.

Thank you to everyone who joined us and showed interest in our work. We look forward to more opportunities to share our passion and advancements with the surgical robotics community!

We’re delighted to share that 6 papers from our lab have been accepted at IEEE ROBIO 2024 (https://lnkd.in/gXQYD7By)!

Our team has made significant contributions to the field of robotic surgery, with a diverse range of research topics

including:

– Neural Rendering

– Gaussian Splatting

– Registration and Reconstruction

– Augmented Reality for surgical planning and training

– Soft Continuum Robots

– Vision-guided surgery

Get a sneak peek at our digest figures below. We’ll be sharing more details soon! If you’re attending #ROBIO2024 in Bangkok,

we’d love to connect and discuss potential collaborations!

Let’s build connections and drive innovation together!

Our paper entitled “LighTDiff: Surgical Endoscopic Image Low-Light Enhancement with T-Diffusion”, led by our former lab member Tong Chen, Qingcheng Lyu, and our PhD student Long Bai, has been awarded the 🎊MICCAI 2024 Best Paper Runner-Up!🎊

This is a collaborative work between the lab of Prof Luping Zhou from The University of Sydney, our #LabREN (http://www.labren.org/mm/), and Qilu Hospital of Shandong University. Congrats to all coauthors!!!

We have also ranked #2nd in the very competitive BraTS Challenge on Sub-Sahara-Africa Adult Glioma (BraTS-SSA)! Our methodology is so-called “Transferring Knowledge from High-Quality to Low-Quality MRI for Adult Glioma Diagnosis”. Stay tuned for more updates!

Congrats to Team members: Yanguang Zhao, Long Bai, Zhaoxi Zhang, and Prof Hongliang Ren from #LabREN at The Chinese University of Hong Kong, Dr. Yanan Wu from China Medical University, and Dr. Mobarakol Islam from University College London.

Robotic endoscopes play a crucial role in diagnosing gastrointestinal disease and performing tumor resections. While current research primarily focuses on autonomously controlling rigid robots, establishing control models for flexible robots remains challenging. To address this, model-free deep reinforcement learning (DRL) presents a promising approach for enabling agents to make decisions under uncertainty.

In this paper, we investigate the control policy of a flexible endoscope using Simulation Open Framework Architecture (SOFA) platform. We design a flexible tendon-driven robotic endoscope (TDRE) and develop a custom simulation environment within SOFA to train DRL agents. Our approach involves implementing the Proximal Policy Optimization (PPO) algorithm to approximate an optimal policy for trajectory planning. The optimal policy facilitates trajectory tracking tasks for the TDRE’s end-effector, such as circle trajectories and action disturbances, without requiring fine-tuning policy network parameters. Experimental results demonstrate that our approach achieves near real-time performance (30 FPS). The feedforward neural network of the policy provides feedback, enabling closed-loop control of TDRE. Furthermore, our experiments show that the navigation success rate of TDRE exceeds 90% within a tolerant error of 3 mm in free space. Notably, compared to direct training with contact, navigation tasks with contact retrained by a pre-trained policy in free space exhibit enhanced navigation capabilities.

Paper link: https://lnkd.in/gDH5xYA5

Co-authors: Chi Kit Ng; Huxin Gao; Tian-Ao Ren; Sam, Jiewen Lai; Hongliang Ren