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Machine learning for white matter fibre tract visualization in the human brain via Mueller matrix polarimetric data

  • Richard McKinley
  • , Leonard A. Felger
  • , Ekkehard Hewer
  • , Theoni Maragkou
  • , Michael Murek
  • , Tatiana Novikova
  • , Omar Rodríguez-Núñez
  • , Angelo Pierangelo
  • , Philippe Schucht
  • University of Bern
  • University Hospital of Lausanne

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

14 Citations (Scopus)

Abstract

A clear identification of the border between a brain tumor and surrounding healthy tissue during neurosurgery is essential in order to maximize tumor resection while preserving neurological function. However, tumor tissue is often difficult to differentiate from infiltrated brain during surgery. Most existing techniques have drawbacks in terms of cost, measurement time and accuracy. The fibre tracts of healthy brain white matter are composed of densely packed bundles of myelinated axons that form uniaxial linear birefringent medium with the optical axis oriented along the direction of the fibre bundle. Brain tumors, whose cells grow in a largely chaotic way, lack this anisotropy of refractive index. Therefore tumor tissue can be distinguished from of healthy white matter using polarized light. A wide-field visible wavelength imaging Mueller polarimetric system was used for the study of formalin-fixed human brain sections measured in reflection geometry. The non-linear decomposition of the Mueller matrices provided the maps of depolarization, scalar retardance and azimuth of the optical axis. A compelling correlation between the azimuth of the optical axis and the orientation of the brain fibre tracts was proven with the gold standard histology analysis. We present the results of post-processing of Mueller polarimetric images of fixed human brain sections using a combination of classical computer vision and machine learning algorithms, for the automated brain fibre tracking in the white matter tracts. Manually labelled polarimetric data was used to train a convolutional neural network to identify white matter. Within the identified white matter, surface fibre tracts could be visualized. We expect that Mueller polarimetric imaging modality combined with our ML algorithms for fibre tracking will visualize the directions of fibre tracts in imaging plane during tumor surgery, thus, allowing a neurosurgeon to orient himself, to spare essential fibre tracts and to make surgery more complete and safe.

Original languageEnglish
Title of host publicationUnconventional Optical Imaging III
EditorsMarc P. Georges, Gabriel Popescu, Nicolas Verrier
PublisherSPIE
ISBN (Electronic)9781510651487
DOIs
Publication statusPublished - 1 Jan 2022
EventUnconventional Optical Imaging III 2022 - Virtual, Online
Duration: 9 May 202220 May 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12136
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceUnconventional Optical Imaging III 2022
CityVirtual, Online
Period9/05/2220/05/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Brain
  • Deep Learning
  • Fibre tracts
  • Machine Learning
  • Mueller polarimetry
  • Unet

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