Advancing automated digital pathology by rapid spectral imaging and AI for nuclear segmentation

Cancer is one of the leading causes of death worldwide and stained tissues’ biopsy analysis remains the standard method for pathology diagnostics. Major optical developments have improved pathological diagnostics lately. High quality microscopic optical scanners now allow whole-slide imaging of tissue sections and with the accessibility of datasets, digital imaging becomes attractive for biopsy analysis. Beyond its user-friendly features for image review and telepathology, digital imaging also enables the utilization of image processing and artificial intelligence (AI) which are advantageous for numerous applications. Nevertheless, AI faces significant barriers to widespread adoption. In order to extend the use of AI for clinical use in pathology, here we present an advanced approach for analyzing Hematoxylin and Eosin-stained biopsies and identify cancer cells. Our approach relies on the fusion of rapid spectral imaging measurements of biopsies with tailored machine learning algorithms designed explicitly for spectral images. The spectrum measured at each pixel provides much more information than standard color, that contains only three values of red, green, and blue intensities. We lately found that the spectral information provides high separability of normal and cancerous cells in Hematoxylin and Eosin-stained biopsies. This breakthrough surpasses previous obstacles, marking the potential for utilizing spectral imaging for cancer identification. Nevertheless, the method required identifying the nuclei in the tissue, a complex task that has not yet been addressed. Here, we demonstrate a rapid spectral imaging system combining artificial intelligence procedures for spectral-based nuclear segmentation using U-net models, with an adjusted input layer for the spectral imaging size. We trained two models; one with the full measured spectrum, and one based on color produced from the spectra. Excellent segmentation performance is achieved with both models, but the model trained on the full spectrum achieved significantly better results. The high performance of spectral image-based segmentation together with the simplicity of the system makes it applicable for tissue analysis and cancer classification in the clinical arena.