# Normalization in Training U-Net for 2D Biomedical Semantic Segmentation

arXiv: https://arxiv.org/abs/1809.03783 · HTML: https://mlsanity.com/paper/1809.03783/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2018.

## Built on by (most cited first, 15 of 15)

- [Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation](https://mlsanity.com/paper/2102.04525/) (2021)
- [Adapting Off-the-Shelf Source Segmenter for Target Medical Image Segmentation](https://mlsanity.com/paper/2106.12497/) (2021)
- [Calibrating the Dice loss to handle neural network overconfidence for biomedical image segmentation](https://mlsanity.com/paper/2111.00528/) (2021)
- [Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation](https://mlsanity.com/paper/2209.07910/) (2022)
- [Incremental Learning for Heterogeneous Structure Segmentation in Brain Tumor MRI](https://mlsanity.com/paper/2305.19404/) (2023)
- [The Challenges and Opportunities of Human-Centered AI for Trustworthy Robots and Autonomous Systems](https://mlsanity.com/paper/2105.04408/) (2021)
- [Incorporating Boundary Uncertainty into loss functions for biomedical image segmentation](https://mlsanity.com/paper/2111.00533/) (2021)
- [CateNorm: Categorical Normalization for Robust Medical Image Segmentation](https://mlsanity.com/paper/2103.15858/) (2021)
- [Multi-organ segmentation: a progressive exploration of learning paradigms under scarce annotation](https://mlsanity.com/paper/2302.03296/) (2023)
- [Gated Linear Model induced U-net for surrogate modeling and uncertainty quantification](https://mlsanity.com/paper/2111.05123/) (2021)
- [On Sensitivity and Robustness of Normalization Schemes to Input Distribution Shifts in Automatic MR Image Diagnosis](https://mlsanity.com/paper/2306.13276/) (2023)
- [Two Deep Learning Approaches for Automated Segmentation of Left Ventricle in Cine Cardiac MRI](https://mlsanity.com/paper/2601.00794/) (2026)
- [Focal Attention Networks: optimising attention for biomedical image segmentation](https://mlsanity.com/paper/2111.00534/) (2021)
- [Diagonal Hierarchical Consistency Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2311.06031/) (2023)
- [A Novel Deep Learning Method for Segmenting the Left Ventricle in Cardiac Cine MRI](https://mlsanity.com/paper/2601.01512/) (2026)

## Abstract (arXiv)

2D biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on Deep Convolutional Neural Network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for biomedical semantic segmentation is the internal covariate shift where the training of convolutional kernels is encumbered by the distribution change of input features, hence both the training speed and performance are decreased. Batch Normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Instance Normalization (IN) and Layer Normalization (LN) have also been proposed. Group Normalization (GN) is proposed more recently and has not yet been applied to 2D biomedical semantic segmentation, however, no specific validations on GN were given. Most DCNNs for biomedical semantic segmentation adopt BN as the normalization method by default, without reviewing its performance. In this paper, four normalization methods - BN, IN, LN and GN are compared in details, specifically for 2D biomedical semantic segmentation. U-Net is adopted as the basic DCNN structure. Three datasets regarding the Right Ventricle (RV), aorta, and Left Ventricle (LV) are used for the validation. The results show that detailed subdivision of the feature map, i.e. GN with a large group number or IN, achieves higher accuracy. This accuracy improvement mainly comes from better model generalization. Codes are uploaded and maintained at Xiao-Yun Zhou's Github.