RGB to Hyperspectral: Spectral Reconstruction for Enhanced Surgical Imaging
This work addresses the need for enhanced surgical imaging to support informed decision-making during procedures, representing an incremental advancement in applying existing methods to new clinical data.
This study tackled the problem of reconstructing hyperspectral signatures from RGB data to enhance surgical imaging, achieving superior performance with transformer models across multiple metrics including RMSE, SAM, PSNR, and SSIM.
This study investigates the reconstruction of hyperspectral signatures from RGB data to enhance surgical imaging, utilizing the publicly available HeiPorSPECTRAL dataset from porcine surgery and an in-house neurosurgery dataset. Various architectures based on convolutional neural networks (CNNs) and transformer models are evaluated using comprehensive metrics. Transformer models exhibit superior performance in terms of RMSE, SAM, PSNR and SSIM by effectively integrating spatial information to predict accurate spectral profiles, encompassing both visible and extended spectral ranges. Qualitative assessments demonstrate the capability to predict spectral profiles critical for informed surgical decision-making during procedures. Challenges associated with capturing both the visible and extended hyperspectral ranges are highlighted using the MAE, emphasizing the complexities involved. The findings open up the new research direction of hyperspectral reconstruction for surgical applications and clinical use cases in real-time surgical environments.