Automatic segmentation of breast tumors from the ultrasound images is essential for the subsequent clinical diagnosis and treatment plan. Although the existing deep learning-based methods have achieved significant progress in automatic segmentation of breast tumor, their performance on tumors with similar intensity to the normal tissues is still not pleasant, especially for the tumor boundaries. To address this issue, we propose a PBNet composed by a multilevel global perception module (MGPM) and a boundary guided module (BGM) to segment breast tumors from ultrasound images. Specifically, in MGPM, the long-range spatial dependence between the voxels in a single level feature maps are modeled, and then the multilevel semantic information is fused to promote the recognition ability of the model for non-enhanced tumors. In BGM, the tumor boundaries are extracted from the high-level semantic maps using the dilation and erosion effects of max pooling, such boundaries are then used to guide the fusion of low and high-level features. Moreover, to improve the segmentation performance for tumor boundaries, a multi-level boundary-enhanced segmentation (BS) loss is proposed. The extensive comparison experiments on both publicly available dataset and in-house dataset demonstrate that the proposed PBNet outperforms the state-of-the-art methods in terms of both qualitative visualization results and quantitative evaluation metrics, with the Dice score, Jaccard coefficient, Specificity and HD95 improved by 0.70%, 1.1%, 0.1% and 2.5% respectively. In addition, the ablation experiments validate that the proposed MGPM is indeed beneficial for distinguishing the non-enhanced tumors and the BGM as well as the BS loss are also helpful for refining the segmentation contours of the tumor.

翻译：从超声图像中自动分割乳腺肿瘤对于后续临床诊断和治疗方案的制定至关重要。尽管现有的深度学习方法在乳腺肿瘤自动分割中取得了显著进展，但其在与正常组织强度相似的肿瘤上的表现仍不尽如人意，尤其是肿瘤边界区域。针对这一问题，本文提出了一种由多层级全局感知模块（MGPM）和边界引导模块（BGM）组成的PBNet，用于从超声图像中分割乳腺肿瘤。具体而言，在MGPM中，首先建模单一层级特征图中体素之间的长程空间依赖关系，然后融合多层级语义信息以增强模型对非增强型肿瘤的识别能力。在BGM中，利用最大池化的膨胀与腐蚀效应从高层语义图中提取肿瘤边界，进而引导低层与高层特征的融合。此外，为提升肿瘤边界的分割性能，本文提出了一种多层级边界增强分割（BS）损失函数。在公开数据集与内部数据集上的广泛对比实验表明，所提出的PBNet在定性可视化结果和定量评估指标上均优于现有最优方法，其中Dice系数、Jaccard系数、特异度及HD95分别提升了0.70%、1.1%、0.1%和2.5%。消融实验进一步验证了MGPM对区分非增强型肿瘤的有效性，同时BGM与BS损失也有助于细化肿瘤的分割轮廓。