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Proceedings Paper

A watershed and feature-based approach for automated detection of lymphocytes on lung cancer images
Author(s): Germán Corredor; Xiangxue Wang; Cheng Lu; Vamsidhar Velcheti; Eduardo Romero; Anant Madabhushi
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Paper Abstract

Automatic detection of lymphocytes could contribute to develop objective measures of the infiltration grade of tumors, which can be used by pathologists for improving the decision making and treatment planning processes. In this article, a simple framework to automatically detect lymphocytes on lung cancer images is presented. This approach starts by automatically segmenting nuclei using a watershed-based approach. Nuclei shape, texture, and color features are then used to classify each candidate nucleus as either lymphocyte or non-lymphocyte by a trained SVM classifier. Validation was carried out using a dataset containing 3420 annotated structures (lymphocytes and non-lymphocytes) from 13 1000 × 1000 fields of view extracted from lung cancer whole slide images. A Deep Learning model was trained as a baseline. Results show an F-score 30% higher with the presented framework than with the Deep Learning approach. The presented strategy is, in addition, more flexible, requires less computational power, and requires much lower training times.

Paper Details

Date Published: 6 March 2018
PDF: 6 pages
Proc. SPIE 10581, Medical Imaging 2018: Digital Pathology, 105810R (6 March 2018); doi: 10.1117/12.2293147
Show Author Affiliations
Germán Corredor, Univ. Nacional de Colombia Sede Bogotá (Colombia)
Case Western Reserve Univ. (United States)
Xiangxue Wang, Case Western Reserve Univ. (United States)
Cheng Lu, Case Western Reserve Univ. (United States)
Vamsidhar Velcheti, Cleveland Clinic (United States)
Eduardo Romero, Univ. Nacional de Colombia Sede Bogotá (Colombia)
Anant Madabhushi, Case Western Reserve Univ. (United States)


Published in SPIE Proceedings Vol. 10581:
Medical Imaging 2018: Digital Pathology
John E. Tomaszewski; Metin N. Gurcan, Editor(s)

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