Supervoxel-Based Segmentation of EM Image Stacks with Learned Shape Features
Immense amounts of high resolution data are now routinely produced thanks to recent advances in EM imaging. While a strong demand for automated analysis now exists, it is stifled by the lack of robust automatic 3D segmentation techniques. State-of-the-art Computer Vision algorithms designed to operate on natural 2D images tend to perform poorly when applied to EM image stacks for a number of reasons. The sheer size of a typical EM image stack renders many segmentation schemes intractable. Most approaches rely on local statistics that easily become confused when confronted with the noise and textures found within EM image stacks. The assumption that strong image gradients always correspond to object boundaries is violated by cluttered membranes belonging to numerous objects. In this work, we propose an automated graph partitioning scheme that addresses these issues. It reduces the computational complexity by operating on supervoxels instead of voxels, incorporates global shape features capable of describing the 3D shape of the target objects, and learns to recognize the distinctive appearance of true boundaries. Our experiments demonstrate that, when applied to segment mitochondria from neural tissue, our approach closely matches the performance of human annotators and outperforms a state-of-the-art 3D segmentation technique.