A bottom-up and top-down model for cell segmentation using multispectral data

Cell segmentation is a challenging problem in histology and cytology that can benefit from additional information obtained in using multispectral imaging. Unique transmission spectra of biological tissues are potentially useful for better classification and segmentation of sub-cellular structures. In this paper, we propose a conditional random field (CRF) model that interprets high-dimensional spectral data during inference and pixel labeling. High quality segmentations are computed by combining low-level cues and high-level contextual information extracted by unsupervised topic discovery. Comparative analysis of the proposed model against the commonly used 2-D CRF model in color space is also performed. Results of this evaluation show the benefits of our proposed model.