Hyperspectral Tissue Image Segmentation Using Semi-Supervised NMF and Hierarchical Clustering.

ABSTRACT: Hyperspectral imaging (HSI) of tissue samples in the mid-infrared (mid-IR) range provides spectro-chemical and tissue structure information at sub-cellular spatial resolution. Disease states can be directly assessed by analyzing the mid-IR spectra of different cell types (e.g., epithelial cells) and sub-cellular components (e.g., nuclei), provided that we can accurately classify the pixels belonging to these components. The challenge is to extract information from hundreds of noisy mid-IR bands at each pixel, where each band is not very informative in itself, making annotations of unstained tissue HSI images particularly tricky. Because the tissue structure is not necessarily identical between the two sections, only a few regions in unstained HSI image can be annotated with high confidence

SUBMITTER: Kumar N 

PROVIDER: S-EPMC6548328 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature