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Proceedings Paper

A method to detect glands in histological gastric cancer images
Author(s): Sunny Alfonso; Germán Corredor; Ricardo Moncayo; Cristian R. Barrera; Angel Y. Sanchez; Paula Toro; Eduardo Romero
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Paper Abstract

Automatic detection and quantification of glands in gastric cancer may contribute to objectively measure the lesion severity, to develop strategies for early diagnosis, and most importantly to improve the patient categorization. This article presents an entire framework for automatic detection of glands in gastric cancer images. This approach starts by selecting gland candidates from a binarized version of the hematoxylin channel. Next, the gland’s shape and nuclei are characterized using local features which feed a Monte Carlo Cross validation method classifier trained previously with manually labeled images. Validation was carried out using a dataset with 1330 annotated structures (2372 glands) from seven fields of view extracted from gastric cancer whole slide images. Results showed an accuracy of 93% using a simple linear classifier. The presented strategy is quite simple, flexible and easily adapted to an actual pathology laboratory.

Paper Details

Date Published: 21 December 2018
PDF: 5 pages
Proc. SPIE 10975, 14th International Symposium on Medical Information Processing and Analysis, 109750X (21 December 2018); doi: 10.1117/12.2511680
Show Author Affiliations
Sunny Alfonso, Univ. Nacional de Colombia (Colombia)
Germán Corredor, Univ. Nacional de Colombia (Colombia)
Ricardo Moncayo, Univ. Nacional de Colombia (Colombia)
Cristian R. Barrera, Univ. Nacional de Colombia (Colombia)
Angel Y. Sanchez, Univ. Nacional de Colombia (Colombia)
Paula Toro, Univ. Nacional de Colombia (Colombia)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 10975:
14th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Jorge Brieva, Editor(s)

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