A supervised learning framework for pancreatic islet segmentation with multi-scale color-texture features and rolling guidance filters

作者：Huang, Yue; Liu, Chi; Eisses, John F.; Husain, Sohail Z.; Rohde, Gustavo K.^*

来源：Cytometry, Part A, 2016, 89A(10): 893-902.

DOI：10.1002/cyto.a.22929

• pancreatic islet
• histopathological image segmentation
• supervised learning
• rolling guidance filter
• multi-scale features

Islet cell quantification and function is important for developing novel therapeutic interventions for diabetes. Existing methods of pancreatic islet segmentation in histopathological images depend strongly on cell/nuclei detection, and thus are limited due to a wide variance in the appearance of pancreatic islets. In this paper, we propose a supervised learning pipeline to segment pancreatic islets in histopathological images, which does not require cell detection. The proposed framework firstly partitions images into superpixels, and then extracts multi-scale color-texture features from each superpixel and processes these features using rolling guidance filters, in order to simultaneously reduce inter-class ambiguity and intra-class variation. Finally, a linear support vector machine (SVM) is trained and applied to segment the testing images. A total of 23 hematoxylin-and-eosin-stained histopathological images with pancreatic islets are used for verifying the framework. With an average accuracy of 95%, training time of 20 min and testing time of 1 min per image, the proposed framework outperforms existing approaches with better segmentation performance and lower computational cost.

• 出版日期2016-10
• 单位厦门大学