Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity. (arXiv:2206.02392v1 [cs.CV] CROSS LISTED)

Web: http://arxiv.org/abs/2206.02392

Morphology of mitochondria plays critical roles in mediating their
physiological functions. Accurate segmentation of mitochondria from 3D electron
microscopy (EM) images is essential to quantitative characterization of their
morphology at the nanometer scale. Fully supervised deep learning models
developed for this task achieve excellent performance but require substantial
amounts of annotated data for training. However, manual annotation of EM images
is laborious and time-consuming because of their large volumes, limited
contrast, and low signal-to-noise ratios (SNRs). To overcome this challenge, …

arxiv cross cv images segmentation semi-supervised