Surgical Workflow Analysis

Minimally invasive surgery using cameras to observe the internal anatomy is the preferred approach to many surgical procedures. Furthermore, other surgical disciplines rely on microscopic images. As a result, endoscopic and microscopic image processing, as well as surgical vision, are evolving as techniques needed to facilitate computer-assisted interventions (CAI). Algorithms that have been reported for such images include 3D surface reconstruction, salient feature motion tracking, instrument detection or activity recognition.

Analyzing the surgical workflow is a prerequisite for many applications in computer-assisted surgery (CAS), such as context-aware visualization of navigation information, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery. Since laparoscopic surgeries are performed using an endoscopic camera, a video stream is always available during surgery, making it the obvious choice as input sensor data for workflow analysis. Furthermore, integrated operating rooms are becoming more prevalent in hospitals, making it possible to access data streams from surgical devices such as cameras, thermoflator, lights, etc. during surgeries.

This project focuses on the online workflow analysis of laparoscopic surgeries. The main goal is to segment surgeries into surgical phases based on the video.

 

Project Phases

  • Designing and developing deep architectures for surgical tools detection and segmentation of colorectal surgeries into surgical phases based on the video input (public dataset)
  • Achieving the highest performance compared to the winners of Endoscopic Vision Challenge-MICCAI 2018
  • Applying the developed technique on prostatectomy (in-house dataset)
  • Detection of deviation from normal habit patterns during surgery
  • Participation in the Endoscopic Vision Challenge-MICCAI 2019

 Prerequisites

  • Enthusiastic Master student in electrical engineering, biomedical engineering, computer science, or a related field
  • Interest in the intersection of machine learning and deep learning
  • Understanding of basic machine learning concepts, image analysis and signal processing
  • Programming experiences in MATLAB and Python
  • A good team player with excellent communication skills
  • A creative solution-finder

Duration: 9 months (BME or ME or MWT)

Start date: a.s.a.p.

Collaboration:  Netherlands Cancer Institute (NKI)

Location: TU/e (Eindhoven) and NKI (Amsterdam)

Contact: For project details, please contact Dr. Behdad Dasht Bozorg, email: B.Dasht.Bozorg@nki.nl

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