Leveraging Surgical Activity Grammar for Primary Intention Prediction in Laparoscopy Procedures

TL;DR

This paper introduces a novel framework that combines surgical activity grammar with visual cues to improve primary intention prediction in laparoscopic procedures, enhancing understanding and automation in robotic surgery.

Contribution

It proposes a hierarchical grammatical approach integrated with visual data for more accurate primary intention recognition in surgical videos, surpassing existing visual-only methods.

Findings

Outperforms existing visual-based activity detectors

Provides a hierarchical grammatical framework for surgical activity analysis

Enhances robotic surgical planning capabilities

Abstract

Surgical procedures are inherently complex and dynamic, with intricate dependencies and various execution paths. Accurate identification of the intentions behind critical actions, referred to as Primary Intentions (PIs), is crucial to understanding and planning the procedure. This paper presents a novel framework that advances PI recognition in instructional videos by combining top-down grammatical structure with bottom-up visual cues. The grammatical structure is based on a rich corpus of surgical procedures, offering a hierarchical perspective on surgical activities. A grammar parser, utilizing the surgical activity grammar, processes visual data obtained from laparoscopic images through surgical action detectors, ensuring a more precise interpretation of the visual information. Experimental results on the benchmark dataset demonstrate that our method outperforms existing surgical activity detectors that rely solely on visual features. Our research provides a promising foundation for developing advanced robotic surgical systems with enhanced planning and automation capabilities.