Hyperspectral imaging of human prostate tissue with automatic segmentation and histologic validation

Hyperspectral imaging (HSI) is an emerging modality that captures rich spatial and spectral information, offering promise for tissue characterization and disease diagnosis. In the context of the prostate, where histopathological assessment remains the gold standard, current imaging techniques often lack sufficient ability to support fine-grained tissue discrimination. Moreover, large annotated HSI datasets are scarce due to the complexity of expert labeling, limiting the development of data-driven diagnostic models. We present a framework for HSI-based analysis of gross prostate datasets, integrating remote annotation for prostate tissue segmentation. HSI data were acquired ex vivo from prostate specimens after radical prostatectomy (RP). Three snapshot HSI cameras were utilized for data acquisition, including a visible (VIS), red/near-infrared (RNIR), and near-infrared (NIR) HSI camera. Digitized histological images were matched to corresponding hyperspectral images. A web-based tool was enabled to facilitate remote annotation on whole-slide images. A spatial-spectral transformer architecture was developed to segment different tissue of the prostate. Our preliminary results show that RNIR achieved the pixel accuracy of 0.694 and NIR yielding a Dice similarity coefficient of 0.601, suggesting that the results can be further improved and that HSI may have potential value in the NIR range. This pilot study provides a pipeline for hyperspectral imaging of human prostate tissue with histologic correlation and validation.