Automatic 3D reconstruction of mitochondrion with local intensity distribution signature and shape feature

Mitochondria play an important role in cellular physiology and synaptic function. Recent electron microscopy (EM) advances make it possible to observe mitochondrial structure on nanoscale, but the attendant massive EM data unfortunately requires months of tedious manual labor. In this paper, we present an automatic approach for the 3D reconstruction of mitochondria from anisotropic EM stack. We first extract a novel local intensity distribution signature (LIDS) feature and learn a random forest classifier (RF) in x-y directions to obtain coarse superpixels. A random disjoint-set forest algorithm can then cluster these superpixels into supervoxels. Next, we use a 3D erosion and dilation method to discard unsatisfying structures, e.g., neural membrane or synapse. At last, the second random forest classifier is learned combining with mitochondrial shape and texture features, which can select the real mitochondria effectively. The confidence values are given to help human experts decide which mitochondrion to be reviewed first. We evaluate the proposed approach on two different anisotropic EM stacks of drosophila brain and compare against the current state-of-the-art methods.