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IntellEditS: Intelligent Learning-Based Editor of Segmentations
Adam P. Harrison1, 2, Neil Birkbeck1, and Michal Sofka1
1Siemens Corporation, Corporate Technology, Princeton, NJ, USA
2University of Alberta, Edmonton, Canada
Abstract. Automatic segmentation techniques, despite demonstrating excellent overall accuracy, can often produce inaccuracies in local regions. As a result, correcting segmentations remains an important task that is often laborious, especially when done manually for 3D datasets. This work presents a powerful tool called Intelligent Learning-Based Editor of Segmentations (IntellEditS) that minimizes user effort and further improves segmentation accuracy. The tool partners interactive learning with an energy-minimization approach to editing. Based on interactive user input, a discriminative classifier is trained and applied to the edited 3D region to produce soft voxel labeling. The labels are integrated into a novel energy functional along with the existing segmentation and image data. Unlike the state of the art, IntellEditS is designed to correct segmentation results represented not only as masks but also as meshes. In addition, IntellEditS accepts intuitive boundary-based user interactions. The versatility and performance of IntellEditS are demonstrated on both MRI and CT datasets consisting of varied anatomical structures and resolutions.
LNCS 8151, p. 235 ff.
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© Springer-Verlag Berlin Heidelberg 2013