Nano-scale context-sensitive semantic segmentation

Nano-scale imaging technologies make it possible to visualize objects at nanometer resolutions. To investigate structures and functions of interest, there is an intrinsic demand for explicit models to extract them from nano-scale data. Segmentation is one of the most critical steps in processing pipelines. However, existing segmentation methods often fail due to extremely low signal-to-noise ratio, low contrast and large data size. In this paper we propose a new context-sensitive method for segmenting three-dimensional volumes. As our method efficiently narrows the search space by using robust context cues, we achieve tractable and reliable nano-scale semantic segmentation. We demonstrate our method on a tomogram of microvilli spikes, for which our method is able to yield accurate spike segmentation and in comparison the state-of-the-art semantic segmentation methods fail due to their inability to handle signal-to-noise ratio and low contrast volumes.