Area of research
Artificial Intelligence · Statistical and Nonlinear Physics
Research interest
Research topics from publications: The structure of the actin-smooth muscle myosin motor domain complex in the rigor state; Nano-scale context-sensitive semantic segmentation; Segmentation by classification: A novel and reliable approach for semi-automatic selection of HIV/SIV envelope spikes. Representative work: 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 ou