Kassim Yasmin M, Maude Richard J, Palaniappan Kannappan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:2736-2739. doi: 10.1109/EMBC.2018.8512764.
Automatic segmentation of vascular network is a critical step in quantitatively characterizing vessel remodeling in retinal images and other tissues. We proposed a deep learning architecture consists of 14 layers to extract blood vessels in fundoscopy images for the popular standard datasets DRIVE and STARE. Experimental results show that our CNN characterized by superior identifying for the foreground vessel regions. It produces results with sensitivity higher by 10% than other methods when trained by the same data set and more than 1% with cross training (trained on DRIVE, tested with STARE and vice versa). Further, our results have better accuracy $> 0 .95$% compared to state of the art algorithms.
血管网络的自动分割是定量表征视网膜图像和其他组织中血管重塑的关键步骤。我们提出了一种由14层组成的深度学习架构，用于在眼底镜图像中提取血管，以处理流行的标准数据集DRIVE和STARE。实验结果表明，我们的卷积神经网络（CNN）在识别前景血管区域方面具有卓越性能。当使用相同数据集训练时，其产生的结果灵敏度比其他方法高10%，交叉训练时（在DRIVE上训练，在STARE上测试，反之亦然）灵敏度高出1%以上。此外，与现有算法相比，我们的结果具有更高的准确率，大于95%。
