Crowd-algorithm collaboration for large-scale endoscopic image annotation with confidence

Research output: Contribution to book/conference proceedings/anthology/reportConference contributionContributedpeer-review

Contributors

  • L. Maier-Hein - , German Cancer Research Center (DKFZ) (Author)
  • T. Ross - , German Cancer Research Center (DKFZ) (Author)
  • J. Gröhl - , German Cancer Research Center (DKFZ) (Author)
  • B. Glocker - , Imperial College London (Author)
  • S. Bodenstedt - , Karlsruhe Institute of Technology (Author)
  • C. Stock - , Heidelberg University  (Author)
  • E. Heim - , German Cancer Research Center (DKFZ) (Author)
  • M. Götz - , German Cancer Research Center (DKFZ) (Author)
  • S. Wirkert - , German Cancer Research Center (DKFZ) (Author)
  • H. Kenngott - , Heidelberg University  (Author)
  • S. Speidel - , Karlsruhe Institute of Technology (Author)
  • K. Maier-Hein - , German Cancer Research Center (DKFZ) (Author)

Abstract

With the recent breakthrough success of machine learning based solutions for automatic image annotation,the availability of reference image annotations for algorithm training is one of the major bottlenecks inmedical image segmentation andmany other fields. Crowdsourcing has evolved as a valuable option for annotating large amounts of data while sparing the resources of experts,yet,segmentation of objects from scratch is relatively time-consuming and typically requires an initialization of the contour. The purpose of this paper is to investigate whether the concept of crowd-algorithm collaboration can be used to simultaneously (1) speed up crowd annotation and (2) improve algorithm performance based on the feedback of the crowd. Our contribution in this context is two-fold: Using benchmarking data from the MICCAI 2015 endoscopic vision challenge we show that atlas forests extended by a novel superpixel-based confidence measure are well-suited for medical instrument segmentation in laparoscopic video data.We further demonstrate that the new algorithm and the crowd can mutually benefit from each other in a collaborative annotation process. Ourmethod can be adapted to various applications and thus holds high potential to be used for large-scale low-cost data annotation.

Details

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsGozde Unal, Sebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells
PublisherSpringer-Verlag
Pages616-623
Number of pages8
ISBN (print)9783319467221
Publication statusPublished - 2016
Peer-reviewedYes
Externally publishedYes

Publication series

SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9901 LNCS
ISSN0302-9743

External IDs

ORCID /0000-0002-4590-1908/work/163294063

Keywords