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

• URL: http://arxiv.org/abs/2507.06647v1
• Date: Wed, 09 Jul 2025 08:24:28 GMT
• Title: ClipGS: Clippable Gaussian Splatting for Interactive Cinematic Visualization of Volumetric Medical Data
• Authors: Chengkun Li, Yuqi Tong, Kai Chen, Zhenya Yang, Ruiyang Li, Shi Qiu, Jason Ying-Kuen Chan, Pheng-Ann Heng, Qi Dou,
• Abstract summary: We introduce ClipGS, an innovative Gaussian splatting framework with the clipping plane supported, for interactive cinematic visualization of medical data.<n>We validate our method on five volumetric medical data, and reach an average 36.635 PSNR rendering quality with 156 FPS and 16.1 MB model size.
• Score: 51.095474325541794
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and Prompts [21.952265898720825]
  We propose a data-driven algorithm that preserves FEM-level accuracy while improving computational efficiency.<n>We introduce a novel human-in-the-loop mechanism into the deformation modeling process.<n>Our algorithm achieves a mean target registration error of 3.42 mm, surpassing state-of-the-art methods in volumetric accuracy.
  arXiv  Detail & Related papers  (2025-06-08T14:19:54Z)
• Surgical Foundation Model Leveraging Compression and Entropy Maximization for Image-Guided Surgical Assistance [50.486523249499115]
  Real-time video understanding is critical to guide procedures in minimally invasive surgery (MIS)<n>We propose Compress-to-Explore (C2E), a novel self-supervised framework to learn compact, informative representations from surgical videos.<n>C2E uses entropy-maximizing decoders to compress images while preserving clinically relevant details, improving encoder performance without labeled data.
  arXiv  Detail & Related papers  (2025-05-16T14:02:24Z)
• Structure-Accurate Medical Image Translation via Dynamic Frequency Balance and Knowledge Guidance [60.33892654669606]
  Diffusion model is a powerful strategy to synthesize the required medical images.<n>Existing approaches still suffer from the problem of anatomical structure distortion due to the overfitting of high-frequency information.<n>We propose a novel method based on dynamic frequency balance and knowledge guidance.
  arXiv  Detail & Related papers  (2025-04-13T05:48:13Z)
• AI-Powered Automated Model Construction for Patient-Specific CFD Simulations of Aortic Flows [8.062885940500259]
  This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images.<n>The proposed approach demonstrates state-of-the-art performance in segmentation and mesh quality while significantly reducing manual effort and processing time.
  arXiv  Detail & Related papers  (2025-03-16T14:18:25Z)
• Multi-Layer Gaussian Splatting for Immersive Anatomy Visualization [1.0580610673031074]
  In medical image visualization, path tracing of volumetric medical data like CT scans produces lifelike visualizations. We propose a novel approach utilizing GS to create an efficient but static intermediate representation of CT scans. Our approach achieves interactive frame rates while preserving anatomical structures, with quality adjustable to the target hardware.
  arXiv  Detail & Related papers  (2024-10-22T12:56:58Z)
• Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing [14.0825980706386]
  Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the local and global dependencies for 3D medical image segmentation. The proposed framework exploits local volume-based self-attention to encode the local dependencies at high resolution and introduces a novel volumetric-mixer to capture the global dependencies at low-resolution feature representations.
  arXiv  Detail & Related papers  (2024-10-20T11:08:38Z)
• Visual Fixation-Based Retinal Prosthetic Simulation [1.0075717342698087]
  The fixation-based framework achieves a classification accuracy of 87.72%, using computational parameters based on a real subject's physiological data. Our approach shows promising potential for producing more semantically understandable percepts with the limited resolution available in retinal prosthetics.
  arXiv  Detail & Related papers  (2024-10-15T15:24:08Z)
• Efficient Deformable Tissue Reconstruction via Orthogonal Neural Plane [58.871015937204255]
  We introduce Fast Orthogonal Plane (plane) for the reconstruction of deformable tissues. We conceptualize surgical procedures as 4D volumes, and break them down into static and dynamic fields comprised of neural planes. This factorization iscretizes four-dimensional space, leading to a decreased memory usage and faster optimization.
  arXiv  Detail & Related papers  (2023-12-23T13:27:50Z)
• Neural LerPlane Representations for Fast 4D Reconstruction of Deformable Tissues [52.886545681833596]
  LerPlane is a novel method for fast and accurate reconstruction of surgical scenes under a single-viewpoint setting. LerPlane treats surgical procedures as 4D volumes and factorizes them into explicit 2D planes of static and dynamic fields. LerPlane shares static fields, significantly reducing the workload of dynamic tissue modeling.
  arXiv  Detail & Related papers  (2023-05-31T14:38:35Z)
• Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis [88.39466012709205]
  This paper builds a novel medical slice to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy. Our method outperforms state-of-the-art algorithms by clear margins.
  arXiv  Detail & Related papers  (2021-12-20T03:38:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.