Automated breast ultrasound image segmentation is essential in a computer-aided diagnosis (CAD) system for breast tumors. In this article, we present a feature pyramid nonlocal network (FPNN) with transform modal ensemble learning (TMEL) for accurate breast tumor segmentation in ultrasound images. Specifically, the FPNN fuses multilevel features under special consideration of long-range dependencies by combining the nonlocal module and feature pyramid network. Additionally, the TMEL is introduced to guide two iFPNNs to extract different tumor details. Two publicly available datasets, i.e., the Dataset-Cairo University and Dataset-Merge, were used for evaluation. The proposed FPNN-TMEL achieves a Dice score of 84.70% ± 0.53%, Jaccard Index (Jac) of 78.10% ± 0.48% and Hausdorff distance (HD) of 2.815 ± 0.016 mm on the Dataset-Cairo University, and Dice of 87.00% ± 0.41%, Jac of 79.16% ± 0.56%, and HD of 2.781±0.035 mm on the Dataset-Merge. Qualitative and quantitative experiments show that our method outperforms other state-of-the-art methods for breast tumor segmentation in ultrasound images. Our code is available at https://github.com/pixixiaonaogou/FPNN-TMEL.

中文翻译：

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具有变换模态集成学习的特征金字塔非局部网络用于超声图像中的乳腺肿瘤分割。

自动乳腺超声图像分割对于乳腺肿瘤的计算机辅助诊断 (CAD) 系统至关重要。在本文中，我们提出了一种具有变换模态集成学习 (TMEL) 的特征金字塔非局部网络 (FPNN)，用于在超声图像中进行精确的乳腺肿瘤分割。具体来说，FPNN 通过结合非局部模块和特征金字塔网络，在特殊考虑远程依赖的情况下融合多级特征。此外，引入了 TMEL 以指导两个 iFPNN 提取不同的肿瘤细节。两个公开可用的数据集，即Dataset-Cairo University 和Dataset-Merge，用于评估。提议的 FPNN-TMEL 在数据集开罗大学上实现了 84.70% ± 0.53% 的 Dice 得分、78.10% ± 0.48% 的 Jaccard 指数 (Jac) 和 2.815 ± 0.016 mm 的 Hausdorff 距离 (HD)，在 Dataset-Merge 上，Dice 为 87.00% ± 0.41%，Jac 为 79.16% ± 0.56%，HD 为 2.781±0.035 mm。定性和定量实验表明，我们的方法优于其他最先进的超声图像乳腺肿瘤分割方法。我们的代码可在 https://github.com/pixixiaonaogou/FPNN-TMEL 获得。