Phase Segmentation Methods for an Automatic Surgical Workflow Analysis

Phase Segmentation Methods for an Automatic Surgical Workflow Analysis

In this paper, we present robust methods for automatically segmenting phases in a specified surgical workflow by using latent Dirichlet allocation (LDA) and hidden Markov model (HMM) approaches. More specifically, our goal is to output an appropriate phase label for each given time point of a surgical workflow in an operating room. The fundamental idea behind our work lies in constructing an HM...

Methods for Automatic Statistical Modeling of Surgical Workflow

An increasing amount of data can be recorded automatically during surgeries. This has led to a recent interest in methods that allow statistical modeling of surgical workflow from this data. One main challenge of these methods is how to automatically generate a statistical model from a set of recorded surgeries. In this paper we will review different methods and show advantages and drawbacks.

A Boosted Segmentation Method for Surgical Workflow Analysis

As demands on hospital efficiency increase, there is a stronger need for automatic analysis, recovery, and modification of surgical workflows. Even though most of the previous work has dealt with higher level and hospital-wide workflow including issues like document management, workflow is also an important issue within the surgery room. Its study has a high potential, e.g., for building contex...

Eye-Gaze Driven Surgical Workflow Segmentation

In today's climate of clinical governance there is growing pressure on surgeons to demonstrate their competence, improve standards and reduce surgical errors. This paper presents a study on developing a novel eye-gaze driven technique for surgical assessment and workflow recovery. The proposed technique investigates the use of a Parallel Layer Perceptor (PLP) to automate the recognition of a ke...

Trajectory Data Mining for Surgical Workflow Analysis