Hyperspectral reconstruction of white light endoscopy images for enhanced segmentation of early esophageal lesions in patients.

Early esophageal squamous neoplasia is difficult to detect under conventional white light imaging due to subtle mucosal and vascular changes. This study utilizes a spectrum-aided vision enhancement (SAVE) framework to computationally reconstruct 401-band hyperspectral information from standard RGB endoscopy images. By integrating these virtual spectral features with a U-Net semantic segmentation model, the approach enhances lesion boundary delineation without requiring specialized hyperspectral hardware. Evaluation on 531 clinical images demonstrates that models using virtual spectral data achieve a mean intersection over union of 74.3%, outperforming conventional white light and narrow-band imaging baselines. Cross-center validation further confirms the generalizability of this model-agnostic enhancement across different endoscopy systems. These findings indicate that virtual hyperspectral reconstruction provides a feasible strategy for enriching diagnostic features in routine clinical workflows. This approach offers a scalable tool for improving the computer-aided detection of early-stage gastrointestinal malignancies.