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Proceedings Paper

A computational framework to detect normal and tuberculosis infected lung from H and E-stained whole slide images
Author(s): M. Khalid Khan Niazi; Gillian Beamer; Metin N. Gurcan
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Paper Abstract

Accurate detection and quantification of normal lung tissue in the context of Mycobacterium tuberculosis infection is of interest from a biological perspective. The automatic detection and quantification of normal lung will allow the biologists to focus more intensely on regions of interest within normal and infected tissues. We present a computational framework to extract individual tissue sections from whole slide images having multiple tissue sections. It automatically detects the background, red blood cells and handwritten digits to bring efficiency as well as accuracy in quantification of tissue sections. For efficiency, we model our framework with logical and morphological operations as they can be performed in linear time. We further divide these individual tissue sections into normal and infected areas using deep neural network. The computational framework was trained on 60 whole slide images. The proposed computational framework resulted in an overall accuracy of 99.2% when extracting individual tissue sections from 120 whole slide images in the test dataset. The framework resulted in a relatively higher accuracy (99.7%) while classifying individual lung sections into normal and infected areas. Our preliminary findings suggest that the proposed framework has good agreement with biologists on how define normal and infected lung areas.

Paper Details

Date Published: 1 March 2017
PDF: 9 pages
Proc. SPIE 10140, Medical Imaging 2017: Digital Pathology, 101400J (1 March 2017); doi: 10.1117/12.2255627
Show Author Affiliations
M. Khalid Khan Niazi, The Ohio State Univ. (United States)
Gillian Beamer, Tufts Univ. (United States)
Metin N. Gurcan, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 10140:
Medical Imaging 2017: Digital Pathology
Metin N. Gurcan; John E. Tomaszewski, Editor(s)

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