SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting

• URL: http://arxiv.org/abs/2506.23309v2
• Date: Tue, 01 Jul 2025 15:26:48 GMT
• Title: SurgTPGS: Semantic 3D Surgical Scene Understanding with Text Promptable Gaussian Splatting
• Authors: Yiming Huang, Long Bai, Beilei Cui, Kun Yuan, Guankun Wang, Mobarak I. Hoque, Nicolas Padoy, Nassir Navab, Hongliang Ren,
• Abstract summary: We present SurgTPGS, a novel text-promptable Gaussian Splatting method to fill this gap.<n>We propose semantic-aware deformation tracking to capture the seamless deformation of semantic features, providing a more precise reconstruction for both texture and semantic features.<n>We conduct comprehensive experiments on two real-world surgical datasets to demonstrate the superiority of SurgTPGS over state-of-the-art methods.
• Score: 45.16104996137126
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In contemporary surgical research and practice, accurately comprehending 3D surgical scenes with text-promptable capabilities is particularly crucial for surgical planning and real-time intra-operative guidance, where precisely identifying and interacting with surgical tools and anatomical structures is paramount. However, existing works focus on surgical vision-language model (VLM), 3D reconstruction, and segmentation separately, lacking support for real-time text-promptable 3D queries. In this paper, we present SurgTPGS, a novel text-promptable Gaussian Splatting method to fill this gap. We introduce a 3D semantics feature learning strategy incorporating the Segment Anything model and state-of-the-art vision-language models. We extract the segmented language features for 3D surgical scene reconstruction, enabling a more in-depth understanding of the complex surgical environment. We also propose semantic-aware deformation tracking to capture the seamless deformation of semantic features, providing a more precise reconstruction for both texture and semantic features. Furthermore, we present semantic region-aware optimization, which utilizes regional-based semantic information to supervise the training, particularly promoting the reconstruction quality and semantic smoothness. We conduct comprehensive experiments on two real-world surgical datasets to demonstrate the superiority of SurgTPGS over state-of-the-art methods, highlighting its potential to revolutionize surgical practices. SurgTPGS paves the way for developing next-generation intelligent surgical systems by enhancing surgical precision and safety. Our code is available at: https://github.com/lastbasket/SurgTPGS.

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