Driving · 2026-10-14
Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics
Tom Olesch, Nan Xiao, Xiongye Xiao, Jonathan C. DeLong, Fei Liu
AI-assisted summary based on the paper. Consult the linked source for methods and limitations. Benchmark numbers appear after editor verification.
TL;DR
The authors forecast surgical tissue deformation under a known future instrument trajectory using an observation-referenced, scene-specific differentiable surface model. Instead of predicting absolute positions, each tissue point is anchored to its last valid observation and only the tool-induced deformation is simulated forward. An optional observation-referenced residual adds graph-spectral spatial modes with periodic harmonic dynamics for structured motion. Evaluated retrospectively on 12 clips, observation referencing gives the largest gain.
Why it matters
Accurate tissue deformation forecasting from limited future visibility could support predictive planning and autonomy in surgical robotics. The observation-referenced formulation aims to reduce sensitivity to reconstruction/initialization errors and persistent simulation offsets, while preserving the predicted deformation response under planned instrument motion.
Method
- Observation-referenced decomposition: forecast future tissue as last valid observed state plus simulated tool-driven displacement (and optional residual correction).
- Scene-specific differentiable, mesh-free position-based dynamics with learned coupling parameters (rigidity, attachment, region scale, grasp adherence) fitted from the observed tool-tissue interaction window.
- Optional observation-referenced periodic residual: graph-spectral spatial modes (graph Laplacian) with amplitudes driven by two harmonics of a shared frequency, applied relative to each point’s last valid observation.
Limitation
Their evaluation measures agreement with offline reconstructed tracks; independent geometric validation is unavailable, and only 2 calibrated stereo clips support physical-unit errors. They report limited generality to 12 development-selected clips (with periodic studies on 3 manipulation pilots and 1 passive recording), and the model assumes fixed grasp and camera with jaw-translation input and effective surface couplings.
Abstract (from arXiv)
Forecasting tissue deformation from future instrument motion could support predictive planning and increasingly autonomous surgical systems. We study this problem using a scene-specific differentiable surface model fitted to an observed tool-tissue interaction window. Rather than forecasting absolute simulated positions, we introduce an \textit{observation-referenced} formulation that anchors each tissue point to its last valid observation and adds only the deformation induced by the known, preplanned future tool trajectory. This reduces sensitivity to reconstruction, initialization, and persistent simulation offsets while preserving the predicted deformation response. The physical model uses differentiable, mesh-free position-based dynamics with scene-specific rigidity, attachment, region scale, and grasp coupling. We further introduce an optional \textit{observation-referenced residual model} combining graph-spectral spatial modes with periodic harmonic dynamics for structured motion not explained by the tool input. We evaluate the framework retrospectively on 12 surgical manipulation clips from three datasets. Observation referencing provides the largest gain, with a median within-clip error ratio of $0.598$ relative to the corresponding absolute simulation forecast. The optional periodic residual further improves forecasting when the remaining motion exhibits periodic structure. These results support observation-referenced deformation prediction as a practical building block for future surgical world models, digital twins, and generative simulators that must predict how tissue evolves under instrument actions.
Related papers
- SurgCast: Action-Conditioned Future Skeletons for Controllable Surgical World Models
- UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map
- SonoWorld: Towards Interactive Pose-Controllable Ultrasound World Models
- PhysLDM: Latent Diffusion for High-Fidelity Deformable Simulation
- DeXplicit: Making Physical State Explicit in Dexterous Manipulation World Models
Publication record
Bibliographic evidence describes where and when a record appeared. It does not validate scientific claims.
First public release: 2026-10-09 · Indexed by Pub-AI: 2026-10-14 UTC
Observation-Referenced Tissue Deformation Forecasting with Tool-Driven and Residual Dynamics
Preprint posted: 2026-10-09 · Original record ↗
- arxiv
- 2610.14191
Explicit source versions (1)
- v1 · 2026-10-09
- Preprint available
Source date: 2026-10-09 · First detected: 2026-10-25 UTC · Historical baseline
Source evidence (1)
- Official evidence ↗ · atom:published (official metadata)
Abstract · arxiv
Forecasting tissue deformation from future instrument motion could support predictive planning and increasingly autonomous surgical systems. We study this problem using a scene-specific differentiable surface model fitted to an observed tool-tissue interaction window. Rather than forecasting absolute simulated positions, we introduce an \textit{observation-referenced} formulation that anchors each tissue point to its last valid observation and adds only the deformation induced by the known, preplanned future tool trajectory. This reduces sensitivity to reconstruction, initialization, and persistent simulation offsets while preserving the predicted deformation response. The physical model uses differentiable, mesh-free position-based dynamics with scene-specific rigidity, attachment, region scale, and grasp coupling. We further introduce an optional \textit{observation-referenced residual model} combining graph-spectral spatial modes with periodic harmonic dynamics for structured motion not explained by the tool input. We evaluate the framework retrospectively on 12 surgical manipulation clips from three datasets. Observation referencing provides the largest gain, with a median within-clip error ratio of $0.598$ relative to the corresponding absolute simulation forecast. The optional periodic residual further improves forecasting when the remaining motion exhibits periodic structure. These results support observation-referenced deformation prediction as a practical building block for future surgical world models, digital twins, and generative simulators that must predict how tissue evolves under instrument actions.