Transformer Based Tissue Classification in Robotic Needle Biopsy

TL;DR

This paper presents a transformer-based method for classifying tissue types during robotic needle biopsies, improving tissue awareness and aiding surgeons in precise needle placement despite tissue deformation.

Contribution

The study introduces a novel transformer model trained on a comprehensive dataset for tissue classification during robotic biopsies, incorporating position and force data.

Findings

Achieved 93% classification accuracy.

Effective identification of tissue transition phases.

Enhanced tissue awareness for surgeons.

Abstract

Image-guided minimally invasive robotic surgery is commonly employed for tasks such as needle biopsies or localized therapies. However, the nonlinear deformation of various tissue types presents difficulties for surgeons in achieving precise needle tip placement, particularly when relying on low-fidelity biopsy imaging systems. In this paper, we introduce a method to classify needle biopsy interventions and identify tissue types based on a comprehensive needle-tissue contact model that incorporates both position and force parameters. We trained a transformer model using a comprehensive dataset collected from a formerly developed robotics platform, which consists of synthetic and porcine tissue from various locations (liver, kidney, heart, belly, hock) marked with interaction phases (pre-puncture, puncture, post-puncture, neutral). This model achieves a significant classification accuracy of 0.93. Our demonstrated method can assist surgeons in identifying transitions to different tissues, aiding surgeons with tissue awareness.