Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Shazia Akbar; Lee Jordan; Alastair M. Thompson; Stephen J. McKenna

doi:10.1109/ISBI.2015.7164111

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Shazia Akbar, Lee Jordan, Alastair M. Thompson, Stephen J. McKenna

Surgical Oncology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

Tumor localization is an important component of histopathology image analysis; it has yet to be reliably automated for breast cancer histopathology. This paper investigates the use of superpixel classification to localize tumor regions. A superpixel representation retains information about visual structures such as cellular compartments, connective tissue, lumen and fatty tissue without having to commit to semantic segmentation at this level. In order to localize tumor in large images, a rotation invariant spatial pyramid representation is proposed using bags-of-superpixels. The method is evaluated on expert-annotated oestrogen-receptor stained TMA spots and compared to other superpixel classification techniques. Results demonstrate that it performs favorably.

Original language	English (US)
Title of host publication	2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015
Publisher	IEEE Computer Society
Pages	1292-1295
Number of pages	4
ISBN (Electronic)	9781479923748
DOIs	https://doi.org/10.1109/ISBI.2015.7164111
State	Published - Jul 21 2015
Event	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 - Brooklyn, United States Duration: Apr 16 2015 → Apr 19 2015

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2015-July
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	12th IEEE International Symposium on Biomedical Imaging, ISBI 2015
Country/Territory	United States
City	Brooklyn
Period	4/16/15 → 4/19/15

Keywords

rotation invariant spatial pyramid
spatial bag-of-words
superpixels
tumor classification

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2015.7164111

Cite this

Akbar, S., Jordan, L., Thompson, A. M., & McKenna, S. J. (2015). Tumor localization in tissue microarrays using rotation invariant superpixel pyramids. In 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015 (pp. 1292-1295). Article 7164111 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July). IEEE Computer Society. https://doi.org/10.1109/ISBI.2015.7164111

Tumor localization in tissue microarrays using rotation invariant superpixel pyramids. / Akbar, Shazia; Jordan, Lee; Thompson, Alastair M. et al.
2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015. IEEE Computer Society, 2015. p. 1292-1295 7164111 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2015-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Akbar, S, Jordan, L, Thompson, AM & McKenna, SJ 2015, Tumor localization in tissue microarrays using rotation invariant superpixel pyramids. in 2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015., 7164111, Proceedings - International Symposium on Biomedical Imaging, vol. 2015-July, IEEE Computer Society, pp. 1292-1295, 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015, Brooklyn, United States, 4/16/15. https://doi.org/10.1109/ISBI.2015.7164111

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abstract = "Tumor localization is an important component of histopathology image analysis; it has yet to be reliably automated for breast cancer histopathology. This paper investigates the use of superpixel classification to localize tumor regions. A superpixel representation retains information about visual structures such as cellular compartments, connective tissue, lumen and fatty tissue without having to commit to semantic segmentation at this level. In order to localize tumor in large images, a rotation invariant spatial pyramid representation is proposed using bags-of-superpixels. The method is evaluated on expert-annotated oestrogen-receptor stained TMA spots and compared to other superpixel classification techniques. Results demonstrate that it performs favorably.",

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Tumor localization in tissue microarrays using rotation invariant superpixel pyramids

Abstract

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Keywords

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