Purpose

The morphological characteristics of retinal vessels are vital for the early diagnosis of pathological diseases such as diabetes and hypertension. However, the low contrast and complex morphology pose a challenge to automatic retinal vessel segmentation. To extract precise semantic features, more convolution and pooling operations are adopted, but some structural information is potentially ignored.

Methods

In the paper, we propose a novel lightweight pyramid network (LPN) fusing multi-scale features with spatial attention mechanism to preserve the structure information of retinal vessels. The pyramid hierarchy model is constructed to generate multi-scale representations, and its semantic features are strengthened with the introduction of the attention mechanism. The combination of multi-scale features contributes to its accurate prediction.

Results

The LPN is evaluated on benchmark datasets DRIVE, STARE and CHASE, and the results indicate its state-of-the-art performance (e.g., ACC of 97.09\(\%\)/97.49\(\%\)/97.48\(\%\), AUC of 98.79\(\%\)/99.01\(\%\)/98.91\(\%\) on the DRIVE, STARE and CHASE datasets, respectively). The robustness and generalization ability of the LPN are further proved in cross-training experiment.

Conclusion

The visualization experiment reveals the semantic gap between various scales of the pyramid and verifies the effectiveness of the attention mechanism, which provide a potential basis for the pyramid hierarchy model in multi-scale vessel segmentation task. Furthermore, the number of model parameters is greatly reduced.

中文翻译：

---

具有空间注意力机制的轻型金字塔网络，可实现精确的视网膜血管分割

目的

视网膜血管的形态特征对于诸如糖尿病和高血压的病理疾病的早期诊断至关重要。然而，低对比度和复杂的形态对自动视网膜血管分割提出了挑战。为了提取精确的语义特征，采用了更多的卷积和池化操作，但是某些结构信息可能会被忽略。

方法

在本文中，我们提出了一种新颖的轻量级金字塔网络（LPN），它将多尺度特征与空间关注机制融合在一起，以保留视网膜血管的结构信息。构建金字塔层次模型以生成多尺度表示，并通过引入注意机制来增强其语义特征。多尺度特征的组合有助于其准确的预测。

结果

LPN在基准数据集DRIVE，STARE和CHASE上进行了评估，结果表明了其最新的性能（例如，ACC为97.09 \（\％\）/ 97.49 \（\％\）/ 97.48 \（ \％\），分别在DRIVE，STARE和CHASE数据集上的AUC为98.79 \ {\％\} /99.01 \ {\％\} /98.91 \ {\％\）。LPN的鲁棒性和泛化能力在交叉训练实验中得到了进一步证明。

结论

可视化实验揭示了金字塔不同尺度之间的语义鸿沟，并验证了注意机制的有效性，为多尺度血管分割任务中的金字塔层次模型提供了潜在的基础。此外，大大减少了模型参数的数量。