With the increasing global concern regarding public health, accurate diagnosis and treatment of diseases have become critical. In the context of liver computed tomography (CT) image diagnosis, obtaining precise liver segmentation output samples can save consultation time and reduce the risk of misdiagnosis. We propose a full-scale connected attention-aware segmentation network, called Attention UNet3+. To fully leverage semantic information at different scales, we redesign the depth supervised decoder and adopt a full-scale skip connection, which can effectively extract features from different layers thus increasing accuracy. The proposed Attention UNet3+ model uses an attention gate connection instead of the skip connection, which effectively suppresses irrelevant regions and highlights salient features of specific local regions during feature extraction, therefore, improving the segmentation accuracy. Additionally, the classification-guided module enhances the liver boundary and reduces over-segmentation of non-liver regions, obtaining accurate segmentation results. Our experimental evaluation on the medical image computing and computer assisted intervention Liver Tumor Segmentation Challenge 2017 dataset shows that the proposed Attention UNet3+ outperforms other improved UNet algorithms for liver image segmentation by a minimum of 2.9% in intersection over union and a minimum of 1.1% in Dice.

中文翻译：

---

Attention UNet3+：用于肝脏 CT 图像分割的全尺寸连接注意力感知 UNet

随着全球对公共卫生的日益关注，疾病的准确诊断和治疗变得至关重要。在肝脏计算机断层扫描（CT）图像诊断中，获得精确的肝脏分割输出样本可以节省会诊时间并降低误诊风险。我们提出了一个全尺寸连接的注意力感知分割网络，称为 Attention UNet3+。为了充分利用不同尺度的语义信息，我们重新设计了深度监督解码器并采用全尺度跳跃连接，可以有效地从不同层提取特征，从而提高准确性。所提出的Attention UNet3+模型使用注意门连接代替跳跃连接，在特征提取过程中有效抑制不相关区域并突出特定局部区域的显着特征，从而提高分割精度。此外，分类引导模块增强了肝脏边界并减少了非肝脏区域的过度分割，获得了准确的分割结果。我们对医学图像计算和计算机辅助干预肝脏肿瘤分割挑战赛 2017 数据集的实验评估表明，所提出的 Attention UNet3+ 在肝脏图像分割方面优于其他改进的 UNet 算法，在交集方面优于并集至少 2.9%，在并集方面优于其他改进的 UNet 算法。骰子。