## 3 Methodology
### Small Kernel Convolution with Effective Receptive Field Shuffle Module (SCER)  
It is widely acknowledged that the larger the receptive field, the larger the range of the original image it can come into contact with, which also means that it may contain more features with a large receptive field and higher-level semantic features. On the other hand, a smaller receptive field indicates that its features tend to be more localized and detailed. In UNet, there are already two 3 \(\times\) 3 \(\times\) 3 convolutions, which lack a large receptive field that can extract features with a large receptive field. Convolutional operations are the fundamental units of UNet; it can extract effective features without additional operations. Increasing the number of stacking convolutional kernels to obtain a large receptive field is undoubtedly a fast and simple method. It is a fact that larger convolutional kernels bring greater receptive fields, but they also bring a larger number of parameters, such as 7 \(\times\) 7 \(\times\) 7. [PERSON] et al. [42] and [PERSON] et al. [43] suggested that the receptive field value of one convolution with kernel 5 \(\times\) 5 \(\times\) 5 is similar to a stack of two 3 \(\times\) 3 \(\times\) 3 convolution layers, and the receptive field value of one convolution with kernel 7 \(\times\) 7 \(\times\) 7 is close to a stack of three 3 \(\times\) 3 \(\times\) 3 convolution layers. However, the difference in their parameter amounts is huge. Taking 2D as an example, the parameters of one convolution with kernel 7 \(\times\) 7 are 49C\({}^{2}\), in which C is the channel number and the parameter count for the two 3 \(\times\) 3 convolutional layers is only 27C\({}^{2}\)[42]. To further reduce the number of parameters brought by the large receptive field, we introduced the idea of lightweight shuffleNetV2 [44], which mainly aims to improve the effect and reduce the number of parameters.  
A diagram of the SCER block is shown in Figure 3. Firstly, in order to reduce the number of parameters in the network, the original image is evenly divided into two branches according to the channel. The operations of the upper branch are used to extract features with an effective receptive field, and the lower one is exploited to preserve the original semantic features, in which the stack of three 3 \(\times\) 3 \(\times\) 3 convolution layers is used to enlarge the receptive field. Next, channel shuffling operations are used to ensure the interaction of information between the two branches. The SCER block can be represented as follows:  
\[Z=shuffle(concat(split(x,\frac{C}{2}),Conv_{3\times 3\times 3}(Conv_{3 \times 3\times 3}(Conv_{3\times 3\times 3}(split(x,\frac{C}{2})))))) \tag{1}\]  
where \(x\) denotes the input images. The _split_ signifies split operations according to parameter \(\frac{C}{2}\), in which \(\frac{C}{2}\) represents the division of images evenly according to the channel. _Conv\({}_{3\times 3\times 3}\)_ is defined as a convolution with kernel 3 \(\times\) 3 \(\times\) 3 and stride of 1. The _shuffle_ is expressed as channel shuffling operations.  
Figure 3: An illustration of building blocks of SCER Block.