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Semantic segmentation of electron microscopy (EM) is an essential step toefficiently obtain reliable morphological statistics. Despite the great successachieved using deep convolutional neural networks (CNNs), they still producecoarse segmentations with lots of discontinuities and false positives formitochondria segmentation. In this study, we introduce a centerline-awaremultitask network by utilizing centerline as an intrinsic shape cue ofmitochondria to regularize the segmentation. Since the application of 3D CNNson large medical volumes is usually hindered by their substantial computationalcost and storage overhead, we introduce a novel hierarchical view-ensembleconvolution (HVEC), a simple alternative of 3D convolution to learn 3D spatialcontexts using more efficient 2D convolutions. The HVEC enables bothdecomposing and sharing multi-view information, leading to increased learningcapacity. Extensive validation results on two challenging benchmarks show that,the proposed method performs favorably against the state-of-the-art methods inaccuracy 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.

HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images