  Semantic segmentation of electron microscopy (EM) is an essential step to
efficiently obtain reliable morphological statistics. Despite the great success
achieved using deep convolutional neural networks (CNNs), they still produce
coarse segmentations with lots of discontinuities and false positives for
mitochondria segmentation. In this study, we introduce a centerline-aware
multitask network by utilizing centerline as an intrinsic shape cue of
mitochondria to regularize the segmentation. Since the application of 3D CNNs
on large medical volumes is usually hindered by their substantial computational
cost and storage overhead, we introduce a novel hierarchical view-ensemble
convolution (HVEC), a simple alternative of 3D convolution to learn 3D spatial
contexts using more efficient 2D convolutions. The HVEC enables both
decomposing and sharing multi-view information, leading to increased learning
capacity. Extensive validation results on two challenging benchmarks show that,
the proposed method performs favorably against the state-of-the-art methods in
accuracy and visual quality but with a greatly reduced model size. Moreover,
the proposed model also shows significantly improved generalization ability,
especially when training with quite limited amount of training data.
