Seeded watershed cut uncertainty estimators for guided interactive segmentation

Watershed cuts are among the fastest segmentation algorithms and therefore well suited for interactive segmentation of very large 3D data sets. To minimize the number of user interactions (“seeds”) required until the result is correct, we want the computer to actively query the human for input at the most critical locations, in analogy to active learning. These locations are found by means of suitable uncertainty measures. We propose various such measures for watershed cuts along with a theoretical analysis of some of their properties. Extensive evaluation on two types of 3D electron microscopic volumes of neural tissue shows that measures which estimate the non-local consequences of new user inputs achieve performance close to an oracle endowed with complete knowledge of the ground truth.

• Type: Journal
• Year: 2012
• Where: CVPR
• Authors: Christoph N. Straehle, Ullrich Köthe, Graham Knott, Kevin L. Briggman, Winfried Denk, Fred A. Hamprecht
• Keywords: Computer Vision, Cvpr 2012, Interactive Segmentation, Segmentation Algorithms, Uncertainty Measures