Advanced Deep Networks for 3D Mitochondria Instance Segmentation

• URL: http://arxiv.org/abs/2104.07961v1
• Date: Fri, 16 Apr 2021 08:27:44 GMT
• Title: Advanced Deep Networks for 3D Mitochondria Instance Segmentation
• Authors: Mingxing Li, Chang Chen, Xiaoyu Liu, Wei Huang, Yueyi Zhang, Zhiwei Xiong
• Abstract summary: We propose two advanced deep networks, named ResUNet-R and ResUNet-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.
• Score: 46.295601731565725
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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