# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
## 2 Related Work
### Deep Learning-Based Approaches  
Recently, many researchers have developed various deep learning-based systems for lung nodule detection and segmentation. The authors of [21] introduced a lung CT image segmentation method using the U-net architecture proposed in [22], consisting of encoder and decoder networks. With the LIDC-IDRI dataset, they achieved a Dice score coefficient (DSC) of 0.9502. It is worth noting that the model presented in [21] and our method are based on U-Net; however, each one is designed for solving a different problem: ref. [21] fine-tuned U-Net to segment the whole lung from the CT images, while our method integrates two new blocks (CAWE and PAWE) with U-Net to improve the segmentation accuracy of nodules. Ref. [10] used a simple version of the U-Net model for lung nodule segmentation by utilizing only two convolutional layers in the encoder network and two deconvolutional layers in the decoder network, U-Net. The model used different receptive field sizes to enhance nodule feature extraction. Their model yields a DSC improvement of 2% compared to the original U-Net. Besides, ref. [9] presented a dual-branch residual network (DB-ResNet) that achieved results similar to [10]. The major differences between [9] and [10] are the use of convolutional blocks of ResNet [23] in the encoder networks and slightly modified pooling layers.  
In turn, ref. [7] combined two U-Net models (named iW-Net) based on user interactions. Their architecture was designed by expecting nodules of only round shapes. The authors combined the weight map and the feature of the model output as a loss function. The iW-Net model gave a final competition performance metric score of 87.27% on nodule detection and a DSC score of 83.10% on nodule segmentation. In addition, ref. [13] presented a multiple resolution residual network (MRRN) that is a modification of the ResNet [23]. The modified MRRN network is used as the backbone of the U-Net model. Ref. [13] achieved a DSC score of 0.76. A slightly transformed version of U-Net called U-Det was presented in [8], where many hidden layers were used to filter the residual blocks located within the encoder and decoder. With the LUNA16 testing dataset, they achieved a DSC of 0.82 that was improved to 0.83 when U-Det applied the Mish activation function proposed in [24] for smoothing and non-monotonic activation.  
Most aforementioned lung nodule segmentation methods use deep learning models that segment nodules from the whole-input CT images. However, this can degrade the segmentation precision. This is because the input images are usually resized before feeding to the deep learning model, which yields too many artifacts and badly affects the objects' boundaries and details. Consequently, in this work, we attempt to build a cascaded lung nodule detection and segmentation system that can outperform the accuracy of the state-of-the-art. Firstly, fine-tuning the important parameters of the state-of-the-art object detection (i.e., Faster-RCNN) is applied to be more appropriate for lung nodule detection and to have an automated system to localize the nodule in CT images. The output of the object detection model is to enable ROI that involves the nodule region. A segmentation model will then be fed with ROIs to segment the exact potential nodule region and properly determine its boundaries. Thus, improving the U-Net model is then achieved by integrating the position attention module (PAWE) and channel attention module (CAWE) to encode contextual information into spatial and channel features. These modules help our segmentation system to accurately distinguish nodules from non-nodules regions and also help in facilitating the model's training process, since they encourage the model to learn nodules' relevant features.