## 1 Introduction  
Breast tumors are a prevalent health concern for women that significantly impacts their well-being and lives. As a result, regular breast screening and diagnosis play a crucial role in formulating effective treatment plans and improving survival rates. Due to the flexibility and convenience of ultrasound imaging, it has become a conventional modality for breast tumor screening. In recent years, many deep learning methods based on ultrasound images have been proposed for breast tumor segmentation. However, complex ultrasound patterns continue to pose the following challenges: (1). blurred boundaries caused by low contrast between the foreground and background; (2). segmentation disruption due to speckle noise (as illustrated in Figure 1). The impressive non-linear learning capability has led to significant successes in medical image segmentation with Full Convolution Network (FCN) and U-Net [1; 2]. Motivated by this, many deep learning approaches have emerged for segmenting breast tumors from ultrasound images. In 2018, [PERSON] et al. [3] were the first to systematically evaluate the impact of different FCN variants on breast tumor segmentation and achieve segmentation results that outperformed traditional methods. AAU-Net [4] integrates a hybrid adaptive attention module instead of the conventional convolution block, enhancing feature extraction across diverse receptive fields. NU-Net [5] utilizes sub-networks of varying depths with shared weights to attain robust representations of breast tumors. Transformer has garnered scholarly attention for its attention mechanism and the complete elimination of convolution. Subsequent investigations [6; 7; 8] have explored the integration of transformer structures in image recognition. Notably, ViT [9] stands out as the pioneering work to apply a pure transformer to image classification, substantiating the viability of transformer architectures for computer vision tasks. In the realm of medical image segmentation, the efficacy of vision transformers has been substantiated by PVT-CASCADE [10] and DuAT [11]. DuAT proposes a Dual-Aggregation Transformer Network to address the challenge of capturing both global and local spatial features, while PVT-CASCADE introduces an innovative attention-based decoder leveraging the multi-stage feature representation of the vision transformer. These advancements underscore the transformative impact of transformer architectures in the medical image field. To further address the issue of blurred boundaries caused by breast tumors, the following optimization strategies have been considered: expanding the receptive field and the attention mechanisms has been widely used. The dilated convolution operation is a commonly used strategy to expand the receptive field. For example, [PERSON] et al. [12] obtained the large receptive field of breast tumors by using dilated convolutions in deeper network layers. In terms of attention mechanisms, [PERSON] et al. [13] proposed a channel attention module to further improve the performance of U-Net for breast tumor segmentation. [PERSON] et al. [14] proposed an attention enhanced U-Net with hybrid dilated convolution, merging dilated convolutions with an attention mechanism. Although progress has been made by these methods, the optimization paradigm from fine to coarse granularity struggles to capture prominent object regions in deeper convolutional layers, where object regions and boundaries stand as two crucial distinguishing features between normal tissue and breast tumors. Thus, we propose an iteratively enhanced Boundary Refinement Module (BRM) based on a global map, emphasizing a pattern from coarse to fine granularity. Our motivation arises from clinical practice, where clinicians initially approximate the location of a breast tumor and then meticulously extract its silhouette mask based on local features. In the NSBR-Net model, we adopt a two-step approach: first predicting the coarse region and then implicitly modeling the boundaries using axial reverse attention.