Neural networks for in situ detection of glioma infiltration using optical coherence tomography

Ronald M. Juarez-Chambi, Carmen Kut, Kaisorn Chaichana, Alfredo Quinones-Hinojosa, Xingde Li, Javier Jo

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

Abstract

In brain cancer surgery, maximal tumor resection improves overall survival and quality of life survival in low-grade and high-grade glioma. Different technologies such as intraoperative magnetic resonance imaging and computed tomography have made major contributions; however, these technologies do not provide quantitative, real-time and three-dimensional continuous guidance. Optical Coherence Tomography (OCT) is a non-invasive, label-free, real-time, high-resolution imaging modality that has been explored for glioma infiltration detection. Here we report a novel Artificial Neural Network (ANN)-based computer-aided diagnosis (CAD) method for automated, real-time, in situ detection of glioma-infiltrated tumor margins. Near 500 volumetric OCT samples were intraoperatively obtained from resected brain tissue specimens of 21 patients with glioma tumors of different stages and labeled as either non-cancerous or glioma-infiltrated based on histopathology evaluation (gold standard). Labeled OCT images from 12 patients were used as training dataset to develop the artificial neural network. Unlabeled OCT images from the other 9 patients were used as a validation dataset to quantify the method detection performance. The CAD system achieved excellent levels of both sensitivity and specificity (∼90%) for detecting glioma-infiltrated tissue with high spatial resolution (∼16 μm laterally). Previous methods for OCT-based detection of glioma-infiltrated brain tissue rely on underlying optical properties such as attenuation coefficient from the OCT signal requiring sacrificing spatial resolution and cumbersome calibration procedures. By overcoming these major challenges, our novel ANN-assisted CAD system will enable implementing practical OCT-guided surgical tools for continuous, real-time and accurate intra-operative detection of glioma-infiltrated brain tissue, facilitating maximal glioma resection and superior surgical outcomes for glioma patients.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
ISBN (Electronic)9781510633957
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: Feb 16 2020Feb 19 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period2/16/202/19/20

Keywords

  • artificial intelligence
  • computed-aided diagnosis
  • deep learning
  • glioma
  • image-guided surgery
  • optical coherence tomography

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., Chaichana, K., Quinones-Hinojosa, A., Li, X., & Jo, J. (2020). Neural networks for in situ detection of glioma infiltration using optical coherence tomography. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113142U] (Progress in Biomedical Optics and Imaging - Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2549612