Multilevel Perception Boundary-guided Network for Breast Lesion
  Segmentation in Ultrasound Images
Xing Yang Jian Zhang Qijian Chen Li Wang Lihui Wang
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.