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© 2015 IEEE. While pathologists can readily elucidate disease-relevant information from tissue images, automated algorithms may fail to capture the intricate details of complex biological specimens. As histology patterns vary depending on different tissue types, it is typically necessary to adapt and optimize segmentation algorithms to specific applications. To address this, we present a supervised machine learning method we call Support Vector Shape Segmentation (SVSS) to enhance and improve more general segmentation methods by utilizing a cell shape ranking function. First, we pose shape segmentation as an optimization problem that maximizes shape similarity with respect to the specific shape classes. Secondly, we propose a computationally efficient algorithm to solve the multi-scale segmentation problem in a minimum number of steps. The main advantage of the approach is that it naturally induces a ranking measure given the set of shape exemplars. We demonstrate large-scale quantitative and qualitative results on epithelial cells in a range of tissue types.

Original publication

DOI

10.1109/ISBI.2015.7164112

Type

Conference paper

Publication Date

01/01/2015

Volume

2015-July

Pages

1296 - 1299