Automated segmentation of electron microcope (EM) images is a challenging problem, but the presence of related images of a different modality can be a valuable resource. This paper describes a method to effectively utilize complementary information, if available, in EM segmentation. Images of both modalities are oversegmented into superpixels. A 2D hidden Markov model (HMM) is set up on the superpixel graph to determine the optimal superpixel mapping between images. This mapping is used to transfer labels and generate preliminary segmentations in the EM domain, whose boundaries are then refined, to eliminate imprecisions due to the superpixel grid, using a 1D HMM based contour refinement method. The performance of the proposed approach is demonstrated on a challenging dataset, and significant improvement is observed over related techniques.
['Renuka Shenoy', 'Min-Chi Shih', 'Kenneth Rose']
Segmentation of cells in electron microscopy images through multimodal label transfer