Simplified labeling process for medical image segmentation

Mingchen Gao, Junzhou Huang, Xiaolei Huang, Shaoting Zhang, Dimitris N. Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations


Image segmentation plays a crucial role in many medical imaging applications by automatically locating the regions of interest. Typically supervised learning based segmentation methods require a large set of accurately labeled training data. However, thel labeling process is tedious, time consuming and sometimes not necessary. We propose a robust logistic regression algorithm to handle label outliers such that doctors do not need to waste time on precisely labeling images for training set. To validate its effectiveness and efficiency, we conduct carefully designed experiments on cervigram image segmentation while there exist label outliers. Experimental results show that the proposed robust logistic regression algorithms achieve superior performance compared to previous methods, which validates the benefits of the proposed algorithms.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
EditorsLe Lu, Antonio Criminisi, Nicholas Ayache, Hervé Delingette, Bjoern H. Menze, Bjoern H. Menze, Georg Langs, Georg Langs, Albert Montillo, Zhuowen Tu, Polina Golland, Kensaku Mori
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783642334177
StatePublished - 2012
Event15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012 - Nice, France
Duration: Oct 5 2012Oct 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7511 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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