  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, we
propose a semi-supervised deep learning model that segments mitochondria by
leveraging the spatial continuity of their structural, morphological, and
contextual information in both labeled and unlabeled images. We use random
piecewise affine transformation to synthesize comprehensive and realistic
mitochondrial morphology for augmentation of training data. Experiments on the
EPFL dataset show that our model achieves performance similar as that of
state-of-the-art fully supervised models but requires only ~20% of their
annotated training data. Our semi-supervised model is versatile and can also
accurately segment other spatially continuous structures from EM images. Data
and code of this study are openly accessible at
https://github.com/cbmi-group/MPP.
