Computer-assisted diagnosis of lung cancer using quantitative topology features

Jiawen Yao, Dheeraj Ganti, Xin Luo, Guanghua Xiao, Yang Xie, Shirley Yan, Junzhou Huang

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

13 Scopus citations

Abstract

In this paper, we proposed a computer-aided diagnosis and analysis for a challenging and important clinical case in lung cancer, i.e., differentiation of two subtypes of Non-small cell lung cancer (NSCLC). The proposed framework utilized both local and topological features from histopathology images. To extract local features, a robust cell detection and segmentation method is first adopted to segment each individual cell in images. Then a set of extensive local features is extracted using efficient geometry and texture descriptors based on cell detection results. To investigate the effectiveness of topological features, we calculated architectural properties from labeled nuclei centroids. Experimental results from four popular classifiers suggest that the cellular structure is very important and the topological descriptors are representative markers to distinguish between two subtypes of NSCLC.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages288-295
Number of pages8
Volume9352
ISBN (Print)9783319248875
DOIs
Publication statusPublished - 2015
Event6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015 - Munich, Germany
Duration: Oct 5 2015Oct 5 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9352
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
CountryGermany
CityMunich
Period10/5/1510/5/15

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ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Yao, J., Ganti, D., Luo, X., Xiao, G., Xie, Y., Yan, S., & Huang, J. (2015). Computer-assisted diagnosis of lung cancer using quantitative topology features. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9352, pp. 288-295). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9352). Springer Verlag. https://doi.org/10.1007/978-3-319-24888-2_35