The topology-overlap trade-off in retinal arteriole-venule segmentation
The topology-overlap trade-off in retinal arteriole-venule segmentation
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DOI：
10.1117/12.2654014
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发表时间：
2023-03
影响因子：
4.9
通讯作者：
Ángel Víctor Juanco-Müller;J. Mota;K. Goatman;C. Hoogendoorn
中科院分区：
文献类型：
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作者：
Ángel Víctor Juanco-Müller;J. Mota;K. Goatman;C. Hoogendoorn
视网膜眼底图像可以是一个宝贵的诊断工具，筛查流行性疾病，如高血压或糖尿病。当它们描绘的小动脉和小静脉被清楚地识别和注释时，它们变得特别有用。然而，这些血管的手动注释非常耗时且费力，需要自动分割。虽然卷积神经网络可以实现预测和专家注释之间的高度重叠，但它们通常无法产生拓扑正确的管状结构预测。这种情况由于分叉与交叉的模糊性而加剧，这会导致分类错误。本文表明，在损失函数中包括拓扑保持项提高了分割血管的连续性，尽管以动脉-静脉错误分类和整体较低的重叠度量为代价。然而，我们表明，通过包括一个方向分数指导的卷积模块，基于各向异性单侧蛋糕小波，我们减少了这种错误分类，并进一步提高了结果的拓扑正确性。我们在公共数据集上使用方便选择的指标来评估我们的模型，以评估重叠和拓扑正确性，表明我们的模型能够从重叠的角度产生与最先进的结果相当的结果，同时提高拓扑准确性。
Retinal fundus images can be an invaluable diagnosis tool for screening epidemic diseases like hypertension or diabetes. And they become especially useful when the arterioles and venules they depict are clearly identified and annotated. However, manual annotation of these vessels is extremely time demanding and taxing, which calls for automatic segmentation. Although convolutional neural networks can achieve high overlap between predictions and expert annotations, they often fail to produce topologically correct predictions of tubular structures. This situation is exacerbated by the bifurcation versus crossing ambiguity which causes classification mistakes. This paper shows that including a topology preserving term in the loss function improves the continuity of the segmented vessels, although at the expense of artery-vein misclassification and overall lower overlap metrics. However, we show that by including an orientation score guided convolutional module, based on the anisotropic single sided cake wavelet, we reduce such misclassification and further increase the topology correctness of the results. We evaluate our model on public datasets with conveniently chosen metrics to assess both overlap and topology correctness, showing that our model is able to produce results on par with state-of-the-art from the point of view of overlap, while increasing topological accuracy.