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.

翻译：自采用深层学习方法以来,Mitochondria通过电子显微镜(EM)图像对Mitochdria例进行分解的工作取得了显著进展,在本文中,我们建议两个先进的深层网络,即Res-UNet-R和Res-UNet-H,从Rat和人类样本中进行3D mitochondria例分解,具体地说,我们设计了一个简单而有效的动脉突变区块,并部署一个多尺度的培训战略,共同促进分解性工作。此外,我们通过在预处理中增加一个分泌操作,提高了测试成套经过训练的模型的通用性。在大规模3D Mitochondria事件分解挑战中,我们的团队在试验阶段结束时在领先板上排名第一。代码可在https://github.com/Limingxing00/MitoEM2021-Challenge查阅。