  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.
