RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation

被引:3
|
作者
Khan, Tariq M. [1 ]
Robles-Kelly, Antonio [1 ]
Naqvi, Syed S. [2 ]
机构
[1] Deakin Univ, Fac Sci Eng & Built Env, Sch IT, Waurn Ponds, Vic 3216, Australia
[2] COMSATS Univ Islamabad, Dept Elect & Comp Eng, Islamabad, Pakistan
来源
2021 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING: TECHNIQUES AND APPLICATIONS (DICTA 2021) | 2021年
关键词
Medical Image Segmentation; Convolutional Neural Networks; Residual Connections; BLOOD-VESSELS; IMAGES;
D O I
10.1109/DICTA52665.2021.9647320
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity. We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. We also used skip connections to keep spatial information loss to a minimum by keeping the number of pooling operations in the network to a minimum. Two publicly available retinal vessel segmentation datasets were used in our experiments. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.
引用
收藏
页码:606 / 612
页数:7
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