The Ki-67 labeling index is a valid and important biomarker to gauge neuroendocrine tumor cell progression. Automatic Ki-67 assessment is very challenging due to complex variations of cell characteristics. In this paper, we propose an integrated learning-based framework for accurate Ki-67 scoring in pancreatic neuroendocrine tumor. The main contributions of our method are: a novel and robust cell detection algorithm is designed to localize both tumor and non-tumor cells; a repulsive deformable model is applied to correct touching cell segmentation; a two stage learning-based scheme combining cellular features and regional structure information is proposed to differentiate tumor from non-tumor cells (such as lymphocytes); an integrated automatic framework is developed to accurately assess the Ki-67 labeling index. The proposed method has been extensively evaluated on 101 tissue microarray (TMA) whole discs, and the cell detection performance is comparable to manual annotations. The automatic Ki-67 score is very accurate compared with pathologists' estimation. © 2013 Springer-Verlag.
CITATION STYLE
Xing, F., Su, H., & Yang, L. (2013). An integrated framework for automatic Ki-67 scoring in pancreatic neuroendocrine tumor. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8149 LNCS, pp. 436–443). https://doi.org/10.1007/978-3-642-40811-3_55
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