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Lecture Notes in Computer Science
Efficient and effective cell segmentation of neuroendocrine tumor (NET) in whole slide scanned images is a difficult task due to a large number of cells. The weak or misleading cell boundaries also present significant challenges. In this paper, we propose a fast, high throughput cell segmentation algorithm by combining top-down shape models and bottom-up image appearance information. A scalable sparse manifold learning method is proposed to model multiple subpopulations of different cell shapedoi:10.1007/978-3-319-24574-4_40 pmid:27924317 pmcid:PMC5136469 fatcat:n4zj7lxe6nhhjhrxqcwwg5rcfe