Cell segmentation and classification by hierarchical supervised shape ranking
Santamaria-Pang A., Rittscher J., Gerdes M., Padfield D.
© 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.