I had the joy of attending AUTOMED & MedRob Symposium organized by imes - Institute of Mechatronic Systems of Leibniz Universität Hannover. Focusing on automation, control, and robotics in medicine, this event was amazing on so many levels:
As a Spotlight Speaker in the “Who-is-who” talks and announced as a 𝘙𝘪𝘴𝘪𝘯𝘨 𝘴𝘵𝘢𝘳, I had the opportunity to present myself and my research. ✨
As Chair of the IEEE Engineering Medicine and Biology Society - Germany/Austria/Switzerland Chapter, we could contribute as a (small) sponsor and I could catch up with the community.
I also co-chaired the session on “Prosthetics & Rehabilitation”. 🦾
Another highlight was receiving the “Fastest Reviewer Award”. 🏃
In robotics, 𝘤𝘭𝘰𝘴𝘦𝘥-𝘭𝘰𝘰𝘱 𝘪𝘯𝘷𝘦𝘳𝘴𝘦 𝘬𝘪𝘯𝘦𝘮𝘢𝘵𝘪𝘤𝘴 are an efficient tool to position the end-effector of rigid manipulators in space - but they quickly encounter limits with soft robots, where not all configurations are attainable through control action. And also, what if we want to reason about the 𝘦𝘯𝘵𝘪𝘳𝘦 𝘴𝘰𝘧𝘵 𝘳𝘰𝘣𝘰𝘵 𝘴𝘩𝘢𝘱𝘦 while solving tasks, not just the end-effector? 🐘 🐙
Endoscopic navigation inside the hollow and deformable urinary bladder is extremely difficult. Tracking suspicious areas with tumors across multiple interventions? Almost impossible due to the lack of stable landmarks.
Excited to be attending the 64th IEEE Conference on Decision and Control in beautiful Rio de Janeiro 🌞 On Wednesday, I present(ed) my work on stabilization of population dynamics in the Session “Estimation and Control of Distributed Parameter Systems III”. Find an extended preprint here.
Elephants have perfected the art of multitasking with their trunks: like a biological Swiss army knife, they can bend, twist, and elongate in ways that rigid robots cannot. Interested in how we can use biomechanical insights for soft robotics? Check out our new preprint ``Shape-space graphs: Fast and collision-free path planning for soft robots’’.
“BlaVeS: A Novel Hand-Labeled Dataset for Improved Bladder Vessel Segmentation with Modified U-Net” by Franziska Krauß. This work tackles the challenges of variable lighting and tissue deformation in endoscopy by proposing a robust U-Net-based model with attention mechanisms, achieving state-of-the-art performance for bladder vessel segmentation. Make sure to check out our corresponding dataset here — the first publicly available dataset for blood vessel segmentation in urinary bladder endoscopy images!
“Multiphysical Tumor Tissue Modeling for Improved Multimodal Sensor-Based Diagnostics” by Matthias Ege. We introduce a comprehensive tissue model that simulates the electro-mechanical behavior of bladder tumors to generate synthetic, multimodal datasets—laying the foundation for better sensor fusion and real-time tissue classification during surgery.
“Tumor Margin Estimation through Simulated Impedivity Mappings Using a Multielectrode Sensor Array” by Zoltan Lovasz. Impedance sensor arrays can help to detect tumorous cell aggregations. This study presents a novel impedivity mapping approach that significantly improves tumor margin estimation accuracy over traditional impedance maps, particularly for complex tumor scenarios.
