Project Summary/Abstract More than 232 million surgeries and interventions are performed each year worldwide, and 51.4 million in the US. Despite improvements in surgical technology and techniques, complication rates of up to 20% are reported. Image guidance is a key factor in improving surgical outcomes and reducing complications. While imaging is routinely used for diagnosis and staging prior to surgery, the majority of the millions of surgical and interventional cases are still performed without a direct link to pre-operative images, instead relying on the operating physician’s decisions based on experience. Image-guided robotic interventions (IGRIs) are being investigated to address those challenges. The underlying hypothesis is that the combined image-guidance and robot-assistance enhance the physician’s ability to see and physically access the lesion with minimal invasion resulting in a better clinical outcome. However, usage of image information in IGRI systems merely follow current conventional non- robotic procedures; those robots are controlled simply based on the geometric information obtained from the image, which leads to a discrepancy between the plan and the actual physical space in vivo due to various uncertainties, including patient motion, heterogenous tissue properties, and communication latencies between system components (e.g., sensors, robot hardware, and software). As a result, IGRI has not shown a clear advantage over conventional non-robotic procedures in terms of clinical outcome. The growing number of IGRI applications and the recognition of the real-world challenge led to a shift of research interest from a hardware- centric approach, which pursues mechanical precision, to a “data-driven” approach, where the treatment is modeled based on data obtained from imaging scanners, sensors, and medical records for personalized planning and control for better clinical outcomes. The data-driven approach requires sizable engineering resources because of the wide range of software components involved. Particularly, integration of software components developed in different fields, i.e., robotics and medical image computing, is challenging due to the lack of robust software platforms that are compatible with every component used. To address this challenge, we will integrate popular medical image computing and robotics software packages, namely Robot Operating System version 2 (ROS2) and 3D Slicer into a single platform. This new platform, called SlicerROS, will be distributed as a plug- in for 3D Slicer. SlicerROS will allow incorporating state-of-the-art robotics and medical image computing tools, which are developed and validated in the robotics and medical image computing communities, into a single IGRI system. We will work on the following aims: (Aim 1) Extend 3D Slicer/ROS integration to achieve seamless integration of medial image computing and robotics tools for data-driven IGRI; (Aim 2) Disseminate SlicerROS in the medical i...