一种基于迁移学习模型的血管分叉检测方法

Xiaoming Liu, Jia Wang, Zhou Yang
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引用次数: 1

摘要

为了帮助临床疾病的诊断,提出了许多不同的眼底图像血管网络检测方法。OCT投影图像中的血管分叉样本非常有限,而相应的眼底图像中的血管分叉样本则很充足。在本文中,我们提出了一种基于迁移学习的方法来检测OCT投影图像中的血管分支。将眼底图像样本与迁移学习技术相结合,用于OCT投影图像的血管分叉检测。实验结果表明,该方法可以提高血管分岔检测的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A vascular bifurcations detection method based on Transfer Learning model
Many distinguished methods for vascular network detection in fundus images were proposed to help the diagnosis of clinical diseases. The vascular bifurcation sample in OCT projection images is quite limited while it is sufficient in the corresponding fundus images. In this paper, we proposed a transfer learning-based method to detect the vascular bifurcations in OCT projection images using supervised transfer learning method. The samples from fundus images are utilized with transfer learning technique for vascular bifurcations detection in OCT projection images. The experimental results show the accuracy of vascular bifurcations detection can be improved by the proposed method.
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