  Although deep encoder-decoder networks have achieved astonishing performance
for mitochondria segmentation from electron microscopy (EM) images, they still
produce coarse segmentations with lots of discontinuities and false positives.
Besides, the need for labor intensive annotations of large 3D dataset and huge
memory overhead by 3D models are also major limitations. To address these
problems, we introduce a multi-task network named EM-Net, which includes an
auxiliary centerline detection task to account for shape information of
mitochondria represented by centerline. Therefore, the centerline detection
sub-network is able to enhance the accuracy and robustness of segmentation
task, especially when only a small set of annotated data are available. To
achieve a light-weight 3D network, we introduce a novel hierarchical
view-ensemble convolution module to reduce number of parameters, and facilitate
multi-view information aggregation.Validations on public benchmark showed
state-of-the-art performance by EM-Net. Even with significantly reduced
training data, our method still showed quite promising results.
