Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
Joint Learning of Vessel Segmentation and Artery/Vein Classification with Post-processing
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发表时间：
2020-01
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通讯作者：
Liangzhi Li;Manisha Verma;Yuta Nakashima;R. Kawasaki;H. Nagahara
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作者：
Liangzhi Li;Manisha Verma;Yuta Nakashima;R. Kawasaki;H. Nagahara
视网膜成像是诊断各种疾病的重要工具。然而，即使对经验丰富的专家来说，阅读视网膜图像也是一项困难且耗时的任务。自动化视网膜图像分析的基本步骤是血管分割和动脉/静脉分类，这提供了潜在疾病的各种信息。为了提高现有视网膜图像自动分析方法的性能，我们提出了一种两步血管分类方法。我们采用基于unet的SeqNet模型从背景中准确分割血管，并对血管类型进行预测。我们的模型是按顺序进行分割和分类的，这样既减轻了标签分布偏差的问题，又便于训练。为了进一步细化分类结果，我们考虑了血管之间的结构信息，对分类结果进行后处理，将高置信度的预测传播到周围的血管。我们的实验表明，我们的方法将分割的AUC提高到0.98，在DRIVE数据集上分类的准确率提高到0.92。
Retinal imaging serves as a valuable tool for diagnosis of various diseases. However, reading retinal images is a difficult and time-consuming task even for experienced specialists. The fundamental step towards automated retinal image analysis is vessel segmentation and artery/vein classification, which provide various information on potential disorders. To improve the performance of the existing automated methods for retinal image analysis, we propose a two-step vessel classification. We adopt a UNet-based model, SeqNet, to accurately segment vessels from the background and make prediction on the vessel type. Our model does segmentation and classification sequentially, which alleviates the problem of label distribution bias and facilitates training. To further refine classification results, we post-process them considering the structural information among vessels to propagate highly confident prediction to surrounding vessels. Our experiments show that our method improves AUC to 0.98 for segmentation and the accuracy to 0.92 in classification over DRIVE dataset.