mlsanityData to 14 Oct 2026

Advanced Deep Networks for 3D Mitochondria Instance Segmentation

arXiv 2104.07961 ↗First posted 16 Apr 2021cs.CVcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 10 of its 21 references that are in the map. First posted on arXiv 16 Apr 2021.

Builds on

10 of 21 references.

References · 10

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  3. 2017Mask R-CNNcited by 7,277
  4. 2017Squeeze-and-Excitation Networkscited by 4,789
  5. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,476
  6. 2019ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networkscited by 575
  7. 2017Video Frame Interpolation via Adaptive Convolutioncited by 158
  8. 2016DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentationcited by 84
  9. 2017Superhuman Accuracy on the SNEMI3D Connectomics Challengecited by 64
  10. 2019EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Networkcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 1; 2023: 3; 2024: 2; 2025: 0; 2026: 1.

8 papers.

  1. 2023Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentationcited by 16
  2. 2022Segmentation in large-scale cellular electron microscopy with deep learning: A literature surveycited by 6
  3. 2021PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomicscited by 3
  4. 20233D Mitochondria Instance Segmentation with Spatio-Temporal Transformerscited by 2
  5. 2023Domain Adaptive Synapse Detection with Weak Point Annotationscited by 2
  6. 2024Graph Relation Distillation for Efficient Biomedical Instance Segmentationcited by 1
  7. 2024MitoSeg: Mitochondria Segmentation Toolcited by 0
  8. 2026μMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EMcited by 0