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. 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 rather than lower losses or a faster training speed.

翻译：2D 生物医学语义分解对于手术中的机器人视觉很重要。 基于深革命神经网络(DCNNN)的分解方法在准确性和自动化水平上都能够超越常规方法。在培训 DCNN 进行生物医学语义分解的过程中,常见的一个共同问题是内部共变式变换,因为输入特征的分布变化阻碍了对卷变内内内核的训练,因此培训速度和性能都有所下降。批次正常化(BN)是处理内部共变转换的首个拟议方法,并被广泛使用。还主要提出了例常态化(IN)和层正常化(LN)的比常规正态分解(GN)优。GN(G)最近又提出了集团正常化(GN),但尚未应用于2D 生物医学分解的分解过程。大多数用于生物医学分解的DCNNN(B)采用正常的分解方法,而没有审查其性能。在本文件中,四种正常化方法 -- BN、IN、IN、LN和G(G)将具体用于2D的分解。 U-R的精度的分解,作为基本的精度的精度的精度改进结果,而用于LLV的分解。用于LV的分解。