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Hyperspectral imaging benchmark based on machine learning for intraoperative brain tumour detection.

Raquel Leon1, Himar Fabelo2,3, Samuel Ortega4,5

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Summary

Hyperspectral imaging (HSI) aids neurosurgeons in identifying brain tumour boundaries during surgery. This technology, combined with machine learning, improves tumour detection and delineation, supporting better patient outcomes.

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Area of Science:

  • Neurosurgery
  • Medical Imaging
  • Machine Learning

Background:

  • Brain tumour resection requires precise identification of tumour margins to maximize removal while preserving healthy tissue.
  • Damage to normal brain tissue during surgery can lead to significant neurological deficits.
  • Hyperspectral imaging (HSI) shows potential as a diagnostic tool in various medical fields.

Purpose of the Study:

  • To evaluate the efficacy of HSI combined with a novel processing framework for intraoperative brain tumour identification and delineation.
  • To establish a machine learning benchmark for in-vivo brain tumour detection using HSI.

Main Methods:

  • A robust k-fold cross-validation approach was employed.
  • An in-vivo brain database of 61 HS images from 34 patients was analyzed.
  • Both spectral and spatial information from HSI data were utilized.

Main Results:

  • The proposed HSI processing framework demonstrated promise for in-vivo brain tumour identification and delineation.
  • A highest median macro F1-Score of 70.2 ± 7.9% was achieved on the test set.
  • The method proved effective for primary (high-grade and low-grade) and secondary brain tumours.

Conclusions:

  • HSI, coupled with the developed processing framework and machine learning, is a promising intraoperative tool for neurosurgery.
  • This approach can assist surgeons in real-time decision-making for improved tumour resection.
  • The study provides a benchmark for future advancements in HSI-based brain tumour delineation.