Surgical Workflow Recognition and Blocking Effectiveness Detection in Laparoscopic Liver Resections with Pringle Maneuver

TL;DR

This paper introduces PmNet, an AI system that recognizes surgical workflows and detects blocking effectiveness in laparoscopic liver resections, using a novel dataset and advanced temporal modeling to improve intraoperative monitoring.

Contribution

The study presents a new dataset PmLR50 and a novel AI model PmNet that effectively recognizes surgical phases and assesses blocking effectiveness in real-time liver surgery.

Findings

PmNet outperforms existing methods on PmLR50 benchmark.

The model accurately recognizes short-term surgical phases.

It effectively detects long-term ischemic states during surgery.

Abstract

Pringle maneuver (PM) in laparoscopic liver resection aims to reduce blood loss and provide a clear surgical view by intermittently blocking blood inflow of the liver, whereas prolonged PM may cause ischemic injury. To comprehensively monitor this surgical procedure and provide timely warnings of ineffective and prolonged blocking, we suggest two complementary AI-assisted surgical monitoring tasks: workflow recognition and blocking effectiveness detection in liver resections. The former presents challenges in real-time capturing of short-term PM, while the latter involves the intraoperative discrimination of long-term liver ischemia states. To address these challenges, we meticulously collect a novel dataset, called PmLR50, consisting of 25,037 video frames covering various surgical phases from 50 laparoscopic liver resection procedures. Additionally, we develop an online baseline for PmLR50, termed PmNet. This model embraces Masked Temporal Encoding (MTE) and Compressed Sequence Modeling (CSM) for efficient short-term and long-term temporal information modeling, and embeds Contrastive Prototype Separation (CPS) to enhance action discrimination between similar intraoperative operations. Experimental results demonstrate that PmNet outperforms existing state-of-the-art surgical workflow recognition methods on the PmLR50 benchmark. Our research offers potential clinical applications for the laparoscopic liver surgery community. Codes are available at https://github.com/RascalGdd/PmNet.