ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data

2025-07-09

AI Summary

To address the high computational cost and low frame rates in interactive cinematic rendering of medical volumetric data, this paper proposes a real-time visualization method based on splittable Gaussian lattices. The method introduces three key innovations: (1) a learnable truncation mechanism that dynamically optimizes the visibility of Gaussian primitives; (2) an adaptive deformation adjustment model enabling dynamic interactions such as clipping-plane manipulation; and (3) an end-to-end rendering framework integrating volumetric preprocessing with efficient spatial indexing. This work is the first to apply Gaussian lattices to real-time cinematic rendering of medical volume data. Evaluated on five CT and anatomical slice datasets, it achieves an average PSNR of 36.64, a rendering speed of 156 FPS, and a compact model size of only 16.1 MB—surpassing state-of-the-art methods in both visual quality and computational efficiency.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Matrix & Tensor Methods
• Cognitive Modeling & Cognitive Systems: Computational Creativity

Application Category

• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

The visualization of volumetric medical data is crucial for enhancing diagnostic accuracy and improving surgical planning and education. Cinematic rendering techniques significantly enrich this process by providing high-quality visualizations that convey intricate anatomical details, thereby facilitating better understanding and decision-making in medical contexts. However, the high computing cost and low rendering speed limit the requirement of interactive visualization in practical applications. In this paper, we introduce ClipGS, an innovative Gaussian splatting framework with the clipping plane supported, for interactive cinematic visualization of volumetric medical data. To address the challenges posed by dynamic interactions, we propose a learnable truncation scheme that automatically adjusts the visibility of Gaussian primitives in response to the clipping plane. Besides, we also design an adaptive adjustment model to dynamically adjust the deformation of Gaussians and refine the rendering performance. We validate our method on five volumetric medical data (including CT and anatomical slice data), and reach an average 36.635 PSNR rendering quality with 156 FPS and 16.1 MB model size, outperforming state-of-the-art methods in rendering quality and efficiency.

Problem

• High computing cost limits interactive medical visualization
• Low rendering speed hinders real-time cinematic rendering
• Dynamic clipping plane interactions require adaptive visibility control

Innovation

• Clippable Gaussian splatting for medical visualization
• Learnable truncation adjusts Gaussian primitive visibility
• Adaptive model refines rendering performance dynamically