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Fast Part-Based Classification for Instrument Detection in Minimally Invasive Surgery

  • Raphael Sznitman
  • Carlos Becker
  • Pascal Fua
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)

Abstract

Automatic visual detection of instruments in minimally invasive surgery (MIS) can significantly augment the procedure experience for operating clinicians. In this paper, we present a novel technique for detecting surgical instruments by constructing a robust and reliable instrument-part detector. While such detectors are typically slow to use, we introduce a novel early stopping scheme for multiclass ensemble classifiers which acts as a cascade and significantly reduces the computational requirements at test time, ultimately allowing it to run at framerate. We evaluate the effectiveness of our approach on instrument detection in retinal microsurgery and laparoscopic image sequences and demonstrate significant improvements in both accuracy and speed.

Keywords

Random Forest Minimally Invasive Surgery Motion Blur Boost Regression Tree Instrument Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Raphael Sznitman
    • 1
  • Carlos Becker
    • 1
  • Pascal Fua
    • 1
  1. 1.École Polytechnique Fédérale de LausanneSwitzerland

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