OCT-DeformNet: Optical Coherence Tomography-Guided Biological Tissue Shape Prediction for Robot Palpation in Microsurgery

Abstract

In medical robotics, biological shape deformation resulting from arbitrary tool-tissue interaction commonly occurs and motivates the need in microsurgery to predict the new geometry of tissue structures. However, handling deformation is challenging due to the lack of a general prediction model for varied surgical scenarios, complex tissue properties, and myriad surgical tool geometries. Limited intraoperative sensors to observe microlevel deformations further compound this difficulty. To solve this problem, this paper proposes the first geometric data-driven framework that uses only the robot palpation tooltip movement and a pre-deformed surface to predict the tissue deformation by using the optical coherence tomography (OCT) sensor. A neural network is trained to learn tooltissue physics and predict the shape from the given robot-tool configurations represented as orientations and displacements. We conducted realistic experiments to verify the models using phantoms of various stiffness and three ex vivo tissue types, with average prediction errors of approximately 0.15 mm and 0.52 mm respectively. This framework provides a general data collection platform for collecting micro-scale palpation data under OCT and can be generalized to soft-tissue related studies in biomedical engineering and surgical robotics research.