TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems. In this paper, we propose, for the first time in workflow analysis, a Multi-Stage Temporal Convolutional Network (MS-TCN) that performs hierarchical prediction refinement for surgical phase recognition. Causal, dilated convolutions allow for a large receptive field and online inference with smooth predictions even during ambiguous transitions. Our method is thoroughly evaluated on two datasets of laparoscopic cholecystectomy videos with and without the use of additional surgical tool information. Outperforming various state-of-the-art LSTM approaches, we verify the suitability of the proposed causal MS-TCN for surgical phase recognition.
Code (2)
Tasks
Surgical phase recognitionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Not End-to-End: Explore Multi-Stage Architecture for Online Surgical Phase Recognition
Surgical phase recognition is of particular interest to computer assisted surgery systems, in which the goal is to predict what phase is occurring at each frame for a surgery video. Networks with multi-stage architecture…
Online surgical phase recognitionSurgical phase recognitionRetrieval of surgical phase transitions using reinforcement learning
In minimally invasive surgery, surgical workflow segmentation from video analysis is a well studied topic. The conventional approach defines it as a multi-class classification problem, where individual video frames are a…
Multi-class Classificationreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1MuST: Multi-Scale Transformers for Surgical Phase Recognition
Phase recognition in surgical videos is crucial for enhancing computer-aided surgical systems as it enables automated understanding of sequential procedural stages. Existing methods often rely on fixed temporal windows f…
Online surgical phase recognitionSurgical phase recognitionMulti-Task Temporal Convolutional Networks for Joint Recognition of Surgical Phases and Steps in Gastric Bypass Procedures
Purpose: Automatic segmentation and classification of surgical activity is crucial for providing advanced support in computer-assisted interventions and autonomous functionalities in robot-assisted surgeries. Prior works…
Activity RecognitionTowards Intelligent Speech Assistants in Operating Rooms: A Multimodal Model for Surgical Workflow Analysis
To develop intelligent speech assistants and integrate them seamlessly with intra-operative decision-support frameworks, accurate and efficient surgical phase recognition is a prerequisite. In this study, we propose a mu…
Surgical phase recognition