Detection of brain tumor margins using optical coherence tomography

Ronald M. Juarez-Chambi, Carmen Kut, Jesus Rico-Jimenez, Daniel U. Campos-Delgado, Alfredo Quinones-Hinojosa, Xingde Li, Javier Jo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationComputer-Aided Diagnosis
PublisherSPIE
Volume10575
ISBN (Electronic)9781510616394
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 12 2018Feb 15 2018

Other

OtherMedical Imaging 2018: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/12/182/15/18

Keywords

  • Brain Cancer
  • Image-guided surgery
  • Machine Learning
  • Optical Coherence Tomography
  • Quadratic Optimization

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Juarez-Chambi, R. M., Kut, C., Rico-Jimenez, J., Campos-Delgado, D. U., Quinones-Hinojosa, A., Li, X., & Jo, J. (2018). Detection of brain tumor margins using optical coherence tomography. In Medical Imaging 2018: Computer-Aided Diagnosis (Vol. 10575). [105751R] SPIE. https://doi.org/10.1117/12.2293599