P10.12.B DEEP LEARNING-BASED NORMALIZATION AND UNMIXING OF HYPERSPECTRAL IMAGES FOR BRAIN TUMOR SURGERY
Bibliographic record
Abstract
Abstract BACKGROUND Hyperspectral Imaging (HSI) for fluorescence-guided brain tumor resection enables better visualization of differences between tissues. This can maximize brain tumor resection, improving patient outcomes. However, much of the processing in HSI uses simplified models that are unable to capture the non-linear, wavelength-dependent phenomena that must be modeled for accurate recovery of fluorophore abundances. To overcome these challenges, measured spectra are normalized to account for artifacts and spectrally unmixed to isolate the protoporphyrin IX (PpIX) signal, which indicates malignant tissue. Existing methods are simplistic and based on phantoms and are unable to account for nonlinear effects such as multiple scattering or the inhomogeneous optical properties of the tissue. We propose a deep learning (DL)-based pipeline encompassing normalization and unmixing, which can fully account for the nonlinear effects and produce more accurate distributions of fluorophore abundances. MATERIAL AND METHODS This study proposes two deep learning models for correction and unmixing, which can capture these effects. Both models use autoencoder-like architectures. While one is trained with protoporphyrin IX (PpIX) concentration labels, the other is semi-supervised, learning hyperspectral unmixing self-supervised and then learning to correct the spectra for optical and geometric effects using a white-light reflectance spectrum in a few-shot manner. The models were evaluated on phantom and pig-brain data with known PpIX concentration. RESULTS The supervised and semi-supervised models achieved Pearson correlation coefficients (phantom, pig-brain) between known and computed PpIX concentrations of (0.997, 0.990) and (0.98, 0.91), respectively, whereas the classical approach achieved (0.93, 0.82). On human data, the semi-supervised model gives qualitatively more realistic results than the classical method, better reducing the variance in PpIX abundance over biopsies that should be relatively homogeneous. CONCLUSION These results show promise for using deep learning to improve HSI in fluorescence-guided neurosurgery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".