Share Email Print
cover

Journal of Medical Imaging

Dimension reduction technique using a multilayered descriptor for high-precision classification of ovarian cancer tissue using optical coherence tomography: a feasibility study
Author(s): Catherine St. Pierre; Wendy-Julie Madore; Étienne De Montigny; Dominique Trudel; Caroline Boudoux; Nicolas Godbout; Anne-Marie Mes-Masson; Kurosh Rahimi; Frédéric Leblond
Format Member Price Non-Member Price
PDF $20.00 $25.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Optical coherence tomography (OCT) yields microscopic volumetric images representing tissue structures based on the contrast provided by elastic light scattering. Multipatient studies using OCT for detection of tissue abnormalities can lead to large datasets making quantitative and unbiased assessment of classification algorithms performance difficult without the availability of automated analytical schemes. We present a mathematical descriptor reducing the dimensionality of a classifier’s input data, while preserving essential volumetric features from reconstructed three-dimensional optical volumes. This descriptor is used as the input of classification algorithms allowing a detailed exploration of the features space leading to optimal and reliable classification models based on support vector machine techniques. Using imaging dataset of paraffin-embedded tissue samples from 38 ovarian cancer patients, we report accuracies for cancer detection <90% for binary classification between healthy fallopian tube and ovarian samples containing cancer cells. Furthermore, multiples classes of statistical models are presented demonstrating <70% accuracy for the detection of high-grade serous, endometroid, and clear cells cancers. The classification approach reduces the computational complexity and needed resources to achieve highly accurate classification, making it possible to contemplate other applications, including intraoperative surgical guidance, as well as other depth sectioning techniques for fresh tissue imaging.

Paper Details

Date Published: 12 October 2017
PDF: 13 pages
J. Med. Imag. 4(4) 041306 doi: 10.1117/1.JMI.4.4.041306
Published in: Journal of Medical Imaging Volume 4, Issue 4
Show Author Affiliations
Catherine St. Pierre, Ecole Polytechnique de Montréal (Canada)
Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)
Wendy-Julie Madore, Ecole Polytechnique de Montréal (Canada)
Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)
Institut du cancer de Montréal (Canada)
Étienne De Montigny, Ecole Polytechnique de Montréal (Canada)
Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)
Dominique Trudel, Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)
Institut du cancer de Montréal (Canada)
Caroline Boudoux, Ecole Polytechnique de Montréal (Canada)
Nicolas Godbout, Ecole Polytechnique de Montréal (Canada)
Anne-Marie Mes-Masson, Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)
Institut du cancer de Montréal (Canada)
Kurosh Rahimi, Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)
Institut du cancer de Montréal (Canada)
Frédéric Leblond, Ecole Polytechnique de Montréal (Canada)
Ctr. de Recherche du Ctr. Hospitalier de l’Univ. de Montréal - CRCHUM (Canada)


© SPIE. Terms of Use
Back to Top