## 1 Introduction  
Semantic image segmentation is one of the most desirable and difficult tasks to analyze medical images. Segmenting a target object in a medical image is important to diagnose and treat various diseases. In clinical practice, manual annotation is still popular for analyzing medical images, but there are some disadvantages, including that it is a time-consuming process that is easily prone to errors. Therefore, precise, faithful, and automatic segmentation is mandatory for improving clinical workflows and supporting quick decision-making for patient treatments. Recently, deep learning approaches, such as convolutional neural networks (CNNs), have been used to obtain an advanced performance in image classification and segmentation [1; 2; 3; 4; 5; 6]. These networks are composed of layers that can learn the undersructure of the information from the data with multiple levels. Deep learning approaches can save time and effort by effectively extracting features by themselves since the features that compose these layers are learned from the data and do not need to be designed by a human. CNNs have achieved promising results in medical analyses including for brain tumor segmentation [7], liver tumor segmentation [8], pancreas segmentation [9], and in computer-aided diagnostic applications [10]. For medical image segmentation, an encoder-decoder architecture network, such as U-Net, is a popular choice [11]. These networks are commonly composed of a downsampling sub-network that captures the high-level features of the images and an upsampling sub-network that rebuilds a pixel-wise segmentation from these high-level features. By using skip connections at multiple scales, they can produce dense predictions in multi-levels. However, despite their strong power of representation, the use of skip connections tends to use redundant information in likable low-level encoder features in a multi-scale approach. Furthermore, the contextual information of the encoder feature at the beginning of the network is insufficient, leading to poor performance for pixel-wise recognition when concatenating with the corresponding high-level decoder feature map. To overcome such above problems, we propose the spatial-channel attention gate (scAG), a novel framework that resolves the weakness of plain skip connection in the segmentation task. It introduces an attention mechanism to emphasize meaningful information along the channel dimension and spatial dimension of the features that are beneficial for segmentation. Specifically, we integrate two-component attention gates into the skip connections of the encoder feature and the corresponding decoder feature of the U-Net model. One is a spatial attention gate (sAG) and the other is a channel attention gate (cAG). By incorporating scAG into a standard encoder-decoder U-Net, the intermediate feature maps from the downsampling path are supposed to be more efficiently utilized to solve the segmentation tasks. sAG automatically concentrates on the region of interest, while cAG automatically learns the representation meaning of the region without additional supervision. The encoder-decoder architecture integrated scAG can be trained end-to-end in a similar way as popular CNNs models. Comprehensive experiments with various medical datasets prove that scAG provides promising results in segmentation tasks. In this research, we tackle the limitations of the U-Net architecture in medical image segmentation. Our contributions are described, as follows:  
1. We attempt to explore the advantages and disadvantages of the popular encoder-decoder U-Net architecture. We then propose a novel spatial-channel attention gate (scAG) while using attention mechanisms to reduce the weakness while maximizing the advantages of using skip connections. 2. The proposed scAG combines two components: the spatial attention gate (sAG) and channel attention gate (cAG).