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 language | English |
|---|---|
| Title of host publication | Unconventional Optical Imaging III |
| Editors | Marc P. Georges, Gabriel Popescu, Nicolas Verrier |
| Publisher | SPIE |
| ISBN (Electronic) | 9781510651487 |
| DOIs | |
| Publication status | Published - 1 Jan 2022 |
| Event | Unconventional Optical Imaging III 2022 - Virtual, Online Duration: 9 May 2022 → 20 May 2022 |
Publication series
| Name | Proceedings of SPIE - The International Society for Optical Engineering |
|---|---|
| Volume | 12136 |
| ISSN (Print) | 0277-786X |
| ISSN (Electronic) | 1996-756X |
Conference
| Conference | Unconventional Optical Imaging III 2022 |
|---|---|
| City | Virtual, Online |
| Period | 9/05/22 → 20/05/22 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Brain
- Deep Learning
- Fibre tracts
- Machine Learning
- Mueller polarimetry
- Unet
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