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Proceedings Paper

Detection of brain tumor margins using optical coherence tomography
Author(s): Ronald M. Juarez-Chambi; Carmen Kut; Jesus Rico-Jimenez; Daniel U. Campos-Delgado; Alfredo Quinones-Hinojosa; Xingde Li; Javier Jo
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Paper Abstract

In brain cancer surgery, it is critical to achieve extensive resection without compromising adjacent healthy, non-cancerous regions. Various technological advances have made major contributions in imaging, including intraoperative magnetic imaging (MRI) and computed tomography (CT). However, these technologies have pros and cons in providing quantitative, real-time and three-dimensional (3D) continuous guidance in brain cancer detection. Optical Coherence Tomography (OCT) is a non-invasive, label-free, cost-effective technique capable of imaging tissue in three dimensions and real time. The purpose of this study is to reliably and efficiently discriminate between non-cancer and cancer-infiltrated brain regions using OCT images. To this end, a mathematical model for quantitative evaluation known as the Blind End- Member and Abundances Extraction method (BEAE). This BEAE method is a constrained optimization technique which extracts spatial information from volumetric OCT images. Using this novel method, we are able to discriminate between cancerous and non-cancerous tissues and using logistic regression as a classifier for automatic brain tumor margin detection. Using this technique, we are able to achieve excellent performance using an extensive cross-validation of the training dataset (sensitivity 92.91% and specificity 98.15%) and again using an independent, blinded validation dataset (sensitivity 92.91% and specificity 86.36%). In summary, BEAE is well-suited to differentiate brain tissue which could support the guiding surgery process for tissue resection.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105751R (27 February 2018); doi: 10.1117/12.2293599
Show Author Affiliations
Ronald M. Juarez-Chambi, Texas A&M Univ. (United States)
Carmen Kut, Johns Hopkins Univ. (United States)
Jesus Rico-Jimenez, Texas A&M Univ. (United States)
Daniel U. Campos-Delgado, Univ. Autónoma de San Luis Potosí (Mexico)
Alfredo Quinones-Hinojosa, Mayo Clinic (United States)
Xingde Li, Johns Hopkins Univ. (United States)
Javier Jo, Texas A&M Univ. (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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