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

• URL: http://arxiv.org/abs/2112.11078v1
• Date: Tue, 21 Dec 2021 10:24:01 GMT
• Title: RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation
• Authors: Tariq M Khan, Antonio Robles-Kelly, Syed S. Naqvi
• Abstract summary: We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.
• Score: 3.0846824529023387
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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