Papers › Advanced Deep Networks for 3D Mitochondria Instance Segmentation
Advanced Deep Networks for 3D Mitochondria Instance Segmentation
Mingxing Li, Chang Chen, Xiaoyu Liu, Wei Huang, Yueyi Zhang, Zhiwei Xiong
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.
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Instance Segmentation | MitoEM | Res-UNet-R/H | AP75-H-Test | 0.829 | #1 of 2 | Archive leaderboard | report |
| 3D Instance Segmentation | MitoEM | Res-UNet-R/H | AP75-H-Val | 0.828 | #1 of 2 | Archive leaderboard | report |
| 3D Instance Segmentation | MitoEM | Res-UNet-R/H | AP75-R-Test | 0.851 | #1 of 2 | Archive leaderboard | report |
| 3D Instance Segmentation | MitoEM | Res-UNet-R/H | AP75-R-Val | 0.917 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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