Journal
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020)
Volume -, Issue -, Pages 1254-1257Publisher
IEEE
DOI: 10.1109/isbi45749.2020.9098403
Keywords
Retinal Vessel Segmentation; Effective Receptive Field; Convolutional Neural Networks
Funding
- National Science Foundation [CNS-1629914, CCF-1640081, CCF-1617735]
- Nanoelectronics Research Corporation, Semiconductor Research Corporation, through Extremely Energy Efficient Collective Electronics, an SRC-NRI Nanoelectronics Research Initiative [2698.004, 2698.005]
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Accurate vessel segmentation in retinal images is vital for retinopathy diagnosis and analysis. However, existence of very thin vessels in low image contrast along with pathological conditions (e.g., capillary dilation or microaneurysms) render the segmentation task difficult. In this work, we present a novel approach for retinal vessel segmentation focusing on improving thin vessel segmentation. We develop a deep convolutional neural network (CNN), which exploits the specific characteristics of the input retinal data to use deep supervision, for improved segmentation accuracy. In particular, we use the average input retinal vessel width and match it with the layer-wise effective receptive fields (LERF) of the CNN to determine the location of the auxiliary supervision. This helps the network to pay more attention to thin vessels, that otherwise the network would 'ignore' during training. We verify our method on three public retinal vessel segmentation datasets (DRIVE, CHASE DB1, and STARE), achieving better sensitivity (10.18% average increase) than state-of-the-art methods while maintaining comparable specificity, accuracy, and AUC.
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