MICCAI 2013 - LNCS 8149-8151

An Integrated Framework for Automatic Ki-67 Scoring in Pancreatic Neuroendocrine Tumor

Fuyong Xing^{1, 2}, Hai Su^{1, 2}, and Lin Yang^{1, 2}

¹Division of Biomedical Informatics, Department of Biostatistics, University of Kentucky, KY 40506, USA

²Department of Computer Science, University of Kentucky, KY 40506, USA

Abstract. 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.

LNCS 8149, p. 436 ff.

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