Alessia Finti

Physically consistent estimation of soft-tissue mechanical properties is critical for surgical robotics, intraoperative safety monitoring, and simulator initialization, yet existing methods typically require force-sensing hardware or manual parameter tuning. This paper presents a physics-informed generative framework that estimates tissue stiffness ($k_s$), damping coefficient ($k_d$), and tool–tissue contact force magnitude ($F_{mag}$) from monocular laparoscopic video in a label-free manner with respect to mechanical parameters and interaction forces. The pipeline integrates three components: DepthPro, a multi-scale Vision Transformer (ViT) for zero-shot metric depth estimation; a 3D geometric contact detection pipeline; and a dual-mode conditional generative network trained via a five-term physics–adversarial loss. A differentiable Mass–Spring–Damper (MSD) simulator is embedded directly in the training loop. This enables gradient-based parameter learning without force-sensor, displacement, or boundary-condition supervision. Parameter identifiability is supported through dual observational grounding: MSD physics consistency against observed contact displacement, and next-frame depth map reconstruction. Validated on CholecSeg8k cholecystectomy sequences, Physics-Informed Neural Network (PINN)-estimated parameters significantly outperform static literature baselines (Wilcoxon p = 2.49 × 10⁻⁸, Cohen’s d = 0.374), with physically plausible viscoelastic settling dynamics recovered within 0.9 s of tool release. Since no force sensors were present at acquisition time, evaluation follows an indirect simulation-consistency protocol. Mechanical parameters are estimated at 1.6 ms/frame, a negligible addition to the monocular depth front end that sets the pipeline rate. Estimated parameters directly enable stiffness-aware haptic rendering, intraoperative safety monitoring, and scene-adapted surgical simulation initialization.

Accurate estimation of tissue biomechanical parameters and tool–tissue interaction forces is fundamental for physically consistent surgical simulation and robotic guidance. This study presents a physics-informed generative framework that jointly estimates the stiffness ( \({k}_s\) ), damping ( \({k}_d\) ), and contact force magnitude ( \(| F |\) ) of a mass–spring–damper (MSD) model directly from laparoscopic depth maps. The proposed model integrates a Physics-Informed Neural Network (PINN) into a generative architecture that enforces physical consistency through the MSD governing equation. Depth information was extracted from annotated surgical videos in the CholecSeg8k dataset. During training, consecutive depth frames are used to infer biomechanical parameters, simulate the corresponding tissue response, and update the network via a combined physics–adversarial loss. Quantitative evaluation demonstrates that parameters predicted by the network yield lower mean squared simulation error compared to default literature values (Wilcoxon p < 0.01), with real-time inference capability (1.6 ms/frame). The proposed method provides a generalizable framework for parameter identification from image-based observations, paving the way toward real-time estimation of hyperelastic tissue properties in surgical settings.