An automated pipeline for mitochondrial segmentation on ATUM-SEM stacks

It is possible now to look more closely into mitochondrial physical structures due to the rapid development of electron microscope (EM). Mitochondrial physical structures play important roles in both cellular physiology and neuronal functions. Unfortunately, the segmentation of mitochondria from EM images has proven to be a difficult and challenging task, due to the presence of various subcellular structures, as well as image distortions in the sophisticated background. Although the current state-of-the-art algorithms have achieved some promising results, they have demonstrated poor performances on these mitochondria which are in close proximity to vesicles or various membranes. In order to overcome these limitations, this study proposes explicitly modelling the mitochondrial double membrane structures, and acquiring the image edges by way of ridge detection rather than by image gradient. In addition, this study also utilizes group-similarity in context to further optimize the local misleading segmentation. Then, the experimental results determined from the images acquired by automated tape-collecting ultramicrotome scanning electron microscopy (ATUM-SEM) demonstrate the effectiveness of this study’s proposed algorithm.