Advances in 3D Slicer: Bridging AI, Cloud Computing, and Interventional Technologies

Abstract

3D Slicer is a customizable, open-source platform widely used for advanced medical image analysis tasks. While existing surveys cover specific applications (e.g., 3D printing, surgical navigation), none have comprehensively addressed the growing number of extensions that integrate AI, cloud computing, and interventional technologies. This gap hinders clinical researchers from fully utilizing the potential of 3D Slicer in their research pipelines. Therefore, this study aims to provide readers with insights into recent advances in 3D Slicer in relevant fields. A narrative review approach was followed in the preparation of the paper. Peer-reviewed articles and preprints published at open-access archives, such as arXiv and medRxiv, were collected. We surveyed approximately 100 studies that proposed 3D Slicer-based applications and covered novel 3D Slicer extensions. The extensions included tools for interactive/fully automatic image segmentation, data annotation, and interfacing with publicly available image repositories and cloud-based computational resources. The developing community of the platform is pushing forward to accelerate the user’s learning process, whereas computational bottlenecks and interface improvements are increasingly needed for a seamless user experience. In conclusion, this paper summarizes the recent advances in 3D Slicer, which integrate AI, cloud computing, and interventional technologies. It has been demonstrated that 3D Slicer is rapidly evolving as a highly adaptable platform in the field of medical image analysis. This study is intended to serve as a reference for clinical researchers seeking to integrate 3D Slicer’s advanced computational capabilities into their research workflows, while understanding both the platform’s expanding potential and current considerations for optimal adoption.