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Automated prostate glandular and nuclei detection using hyperspectral imaging

Detection and segmentation of glandular structures are important, since these structures contain clinical relevant information regarding the disease status and Gleason grade of prostate cancer. Manual gland segmentation process is very time consuming and subjective, also existing automated methods are not robust and reliable. We set out to design an automated, fast and objective method. In this paper we present an automated methodology for automated detection of structures of interest in digitalized histopathology images of a Tissue Micro Array (TMA). We show a successful method for detection of prostate glandular structures and its nuclei. Our method integrates different techniques: (1) construct hyperspectral transmission images using sixteen light wavelengths, (2) use Principal Component Analysis (PCA) to construct new RGB images, (3) use clustering to segment different structures in an unsupervised fashion, and (4) apply post-processing morphological cleaning as the final step in our pipeline. We detected 80% plus of the glandular structure in 61% of cores, 80% –50% of the glands in 15% of cores and less than 50% of the glands in 24% of cores.