Evidence map›Paper›PMID 41376807›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2022

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

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Near-real-time Mueller polarimetric image processing for neurosurgical intervention.International journal of computer assisted radiology and surgery · 2024
    Article
  4. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Richard McKinleySupport Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Leonard A FelgerDepartment of Neurosurgery, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Ekkehard HewerLausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
Theoni MaragkouInstitute for Pathology, University of Bern, Bern, Switzerland.
Michael MurekDepartment of Neurosurgery, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Tatiana NovikovaLPICM, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, France.
Omar Rodríguez-NúñezDepartment of Neurosurgery, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.
Angelo PierangeloLPICM, CNRS, Ecole Polytechnique, Institut Polytechnique de Paris, France.
Philippe SchuchtDepartment of Neurosurgery, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland.

Funding

Swiss National Science Foundation 205904
6 · The paper itself

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.

Indexed as

BrainDeep LearningFibre tractsMachine LearningMueller polarimetryUnet

Identifiers

PMID41376807
PMCPMC7618445

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.