Background and objective

Accurate segmentation of breast mass in 3D automated breast ultrasound (ABUS) images plays an important role in qualitative and quantitative ABUS image analysis. Yet this task is challenging due to the low signal to noise ratio and serious artifacts in ABUS images, the large shape and size variation of breast masses, as well as the small training dataset compared with natural images. The purpose of this study is to address these difficulties by designing a dilated densely connected U-Net (D²U-Net) together with an uncertainty focus loss.

Methods

A lightweight yet effective densely connected segmentation network is constructed to extensively explore feature representations in the small ABUS dataset. In order to deal with the high variation in shape and size of breast masses, a set of hybrid dilated convolutions is integrated into the dense blocks of the D²U-Net. We further suggest an uncertainty focus loss to put more attention on unreliable network predictions, especially the ambiguous mass boundaries caused by low signal to noise ratio and artifacts. Our segmentation algorithm is evaluated on an ABUS dataset of 170 volumes from 107 patients. Ablation analysis and comparison with existing methods are conduct to verify the effectiveness of the proposed method.

Results

Experiment results demonstrate that the proposed algorithm outperforms existing methods on 3D ABUS mass segmentation tasks, with Dice similarity coefficient, Jaccard index and 95% Hausdorff distance of 69.02%, 56.61% and 4.92 mm, respectively.

Conclusions

The proposed method is effective in segmenting breast masses on our small ABUS dataset, especially breast masses with large shape and size variations.

中文翻译：

---

用于 3D ABUS 质量分割的具有不确定焦点损失的膨胀密集连接 U-Net

背景和目的

在 3D 自动乳房超声 (ABUS) 图像中准确分割乳房肿块在定性和定量 ABUS 图像分析中起着重要作用。然而，由于 ABUS 图像的低信噪比和严重的伪影、乳房肿块的大形状和大小变化以及与自然图像相比的小训练数据集，这项任务具有挑战性。本研究的目的是通过设计一个扩张的密集连接的 U-Net (D ² U-Net) 以及不确定的焦点损失来解决这些困难。

方法

构建了一个轻量级但有效的密集连接分割网络，以广泛探索小型 ABUS 数据集中的特征表示。为了处理乳房肿块形状和大小的高度变化，将一组混合扩张卷积集成到 D ² U-Net的密集块中。我们进一步建议不确定性焦点损失，以将更多注意力放在不可靠的网络预测上，尤其是由低信噪比和伪影引起的模糊质量边界。我们的分割算法是在来自 107 名患者的 170 个卷的 ABUS 数据集上进行评估的。进行消融分析并与现有方法进行比较，以验证所提出方法的有效性。

结果

实验结果表明，该算法在 3D ABUS 质量分割任务上优于现有方法，Dice 相似系数、Jaccard 指数和 95% Hausdorff 距离分别为 69.02%、56.61% 和 4.92 mm。

结论

所提出的方法在我们的小型 ABUS 数据集上分割乳房肿块是有效的，尤其是具有较大形状和大小变化的乳房肿块。