# Advanced Deep Networks for 3D Mitochondria Instance Segmentation

arXiv: https://arxiv.org/abs/2104.07961 · HTML: https://mlsanity.com/paper/2104.07961/

## 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 (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019)
- [Video Frame Interpolation via Adaptive Convolution](https://mlsanity.com/paper/1703.07514/) (2017)
- [DCAN: Deep Contour-Aware Networks for Accurate Gland Segmentation](https://mlsanity.com/paper/1604.02677/) (2016)
- [Superhuman Accuracy on the SNEMI3D Connectomics Challenge](https://mlsanity.com/paper/1706.00120/) (2017)
- [EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network](https://mlsanity.com/paper/1912.00201/) (2019)

## Built on by (most cited first, 8 of 8)

- [Learning Multiscale Consistency for Self-supervised Electron Microscopy Instance Segmentation](https://mlsanity.com/paper/2308.09917/) (2023)
- [Segmentation in large-scale cellular electron microscopy with deep learning: A literature survey](https://mlsanity.com/paper/2206.07171/) (2022)
- [PyTorch Connectomics: A Scalable and Flexible Segmentation Framework for EM Connectomics](https://mlsanity.com/paper/2112.05754/) (2021)
- [3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers](https://mlsanity.com/paper/2303.12073/) (2023)
- [Domain Adaptive Synapse Detection with Weak Point Annotations](https://mlsanity.com/paper/2308.16461/) (2023)
- [Graph Relation Distillation for Efficient Biomedical Instance Segmentation](https://mlsanity.com/paper/2401.06370/) (2024)
- [MitoSeg: Mitochondria Segmentation Tool](https://mlsanity.com/paper/2409.11974/) (2024)
- [μMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM](https://mlsanity.com/paper/2606.21605/) (2026)

## 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.