# EM-NET: Centerline-Aware Mitochondria Segmentation in EM Images via Hierarchical View-Ensemble Convolutional Network

arXiv: https://arxiv.org/abs/1912.00201 · HTML: https://mlsanity.com/paper/1912.00201/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2019.

## Built on by (most cited first, 5 of 5)

- [Medical Image Segmentation with Limited Supervision: A Review of Deep Network Models](https://mlsanity.com/paper/2103.00429/) (2021)
- [Advanced Deep Networks for 3D Mitochondria Instance Segmentation](https://mlsanity.com/paper/2104.07961/) (2021)
- [HIVE-Net: Centerline-Aware HIerarchical View-Ensemble Convolutional Network for Mitochondria Segmentation in EM Images](https://mlsanity.com/paper/2101.02877/) (2021)
- [3D Mitochondria Instance Segmentation with Spatio-Temporal Transformers](https://mlsanity.com/paper/2303.12073/) (2023)
- [Semi-Supervised Segmentation of Mitochondria from Electron Microscopy Images Using Spatial Continuity](https://mlsanity.com/paper/2206.02392/) (2022)

## Abstract (arXiv)

Although deep encoder-decoder networks have achieved astonishing performance for mitochondria segmentation from electron microscopy (EM) images, they still produce coarse segmentations with lots of discontinuities and false positives. Besides, the need for labor intensive annotations of large 3D dataset and huge memory overhead by 3D models are also major limitations. To address these problems, we introduce a multi-task network named EM-Net, which includes an auxiliary centerline detection task to account for shape information of mitochondria represented by centerline. Therefore, the centerline detection sub-network is able to enhance the accuracy and robustness of segmentation task, especially when only a small set of annotated data are available. To achieve a light-weight 3D network, we introduce a novel hierarchical view-ensemble convolution module to reduce number of parameters, and facilitate multi-view information aggregation.Validations on public benchmark showed state-of-the-art performance by EM-Net. Even with significantly reduced training data, our method still showed quite promising results.