Background and objective: With the advancement of electron microscopy (EM) imaging technology, neuroscientists can investigate the function of various intracellular organelles, e.g, mitochondria, at nano-scale. 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.

Methods: 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.

Results: 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. Detailed sensitivity analysis and ablation study have also been conducted, which show the robustness of the proposed model and effectiveness of the proposed modules.

Conclusions: The experiments highlighted that the proposed architecture enables both simplicity and efficiency leading to increased capacity of learning spatial contexts. Moreover, incorporating shape cues such as centerline information is a promising approach to improve the performance of mitochondria segmentation.

背景与目的：随着电子显微镜（EM）成像技术的发展，神经科学家可以纳米尺度研究各种细胞内细胞器的功能，例如线粒体。电子显微镜（EM）的语义分割是有效获取可靠的形态统计数据的必要步骤。尽管使用深度卷积神经网络（CNN）取得了巨大的成功，但它们仍然会产生具有很多不连续性的粗略分割和线粒体分割的误报。

方法：在本研究中，我们通过利用中心线作为线粒体的固有形状提示来引入可感知中心线的多任务网络，以规范化分割。由于3D CNN在大型医疗量中的应用通常会因其大量的计算成本和存储开销而受到阻碍，因此我们引入了一种新颖的分层视图-整体卷积（HVEC），这是3D卷积的一种简单替代方案，可以使用更高效的2D学习3D空间上下文卷积。HVEC能够分解和共享多视图信息，从而提高了学习能力。

结果：在两个具有挑战性的基准上进行的广泛验证结果表明，所提出的方法在准确性和视觉质量方面优于最新方法，但模型尺寸大大减小。此外，提出的模型还显示出显着提高的泛化能力，尤其是在训练数据量非常有限的情况下。还进行了详细的灵敏度分析和消融研究，显示了所提出模型的鲁棒性和所提出模块的有效性。

结论：实验强调了所提出的体系结构既实现了简单性又提高了效率，从而提高了学习空间上下文的能力。而且，合并诸如中心线信息之类的形状提示是一种改善线粒体分割性能的有前途的方法。