  Mitochondria instance segmentation from electron microscopy (EM) images has
seen notable progress since the introduction of deep learning methods. In this
paper, we propose two advanced deep networks, named Res-UNet-R and Res-UNet-H,
for 3D mitochondria instance segmentation from Rat and Human samples.
Specifically, we design a simple yet effective anisotropic convolution block
and deploy a multi-scale training strategy, which together boost the
segmentation performance. Moreover, we enhance the generalizability of the
trained models on the test set by adding a denoising operation as
pre-processing. In the Large-scale 3D Mitochondria Instance Segmentation
Challenge, our team ranks the 1st on the leaderboard at the end of the testing
phase. Code is available at
https://github.com/Limingxing00/MitoEM2021-Challenge.
