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 at ISBI 2021, our method ranks the 1st place. Code is available at https://github.com/Limingxing00/MitoEM2021-Challenge.
@article{arxiv.2104.07961,
title = {Advanced Deep Networks for 3D Mitochondria Instance Segmentation},
author = {Mingxing Li and Chang Chen and Xiaoyu Liu and Wei Huang and Yueyi Zhang and Zhiwei Xiong},
journal= {arXiv preprint arXiv:2104.07961},
year = {2022}
}