Simplified labeling process for medical image segmentation

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

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

3 Citations (Scopus)

Abstract

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
EditorsNicholas Ayache, Hervé Delingette, Kensaku Mori, Polina Golland
PublisherSpringer Verlag
Pages387-394
Number of pages8
ISBN (Print)9783642334177
StatePublished - Jan 1 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

Conference

Conference15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period10/5/1210/5/12

Fingerprint

Medical Image
Image segmentation
Image Segmentation
Labeling
Robust Regression
Logistic Regression
Outlier
Logistics
Labels
Medical Imaging
Supervised learning
Medical imaging
Supervised Learning
Region of Interest
Large Set
Segmentation
Necessary
Experimental Results
Experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Gao, M., Huang, J., Huang, S. X., Zhang, S., & Metaxas, D. N. (2012). Simplified labeling process for medical image segmentation. In N. Ayache, H. Delingette, K. Mori, & P. Golland (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings (pp. 387-394). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS). Springer Verlag.
Gao, Mingchen ; Huang, Junzhou ; Huang, Sharon Xiaolei ; Zhang, Shaoting ; Metaxas, Dimitris N. / Simplified labeling process for medical image segmentation. Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. editor / Nicholas Ayache ; Hervé Delingette ; Kensaku Mori ; Polina Golland. Springer Verlag, 2012. pp. 387-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Gao, M, Huang, J, Huang, SX, Zhang, S & Metaxas, DN 2012, Simplified labeling process for medical image segmentation. in N Ayache, H Delingette, K Mori & P Golland (eds), Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7511 LNCS, Springer Verlag, pp. 387-394, 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012, Nice, France, 10/5/12.

Simplified labeling process for medical image segmentation. / Gao, Mingchen; Huang, Junzhou; Huang, Sharon Xiaolei; Zhang, Shaoting; Metaxas, Dimitris N.

Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. ed. / Nicholas Ayache; Hervé Delingette; Kensaku Mori; Polina Golland. Springer Verlag, 2012. p. 387-394 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7511 LNCS).

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

TY - GEN

T1 - Simplified labeling process for medical image segmentation

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N2 - 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.

AB - 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.

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M3 - Conference contribution

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Gao M, Huang J, Huang SX, Zhang S, Metaxas DN. Simplified labeling process for medical image segmentation. In Ayache N, Delingette H, Mori K, Golland P, editors, Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings. Springer Verlag. 2012. p. 387-394. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).