Abstract
In a fundus image, Vessel local characteristics like direction, illumination and noise vary considerably, making vessel segmentation a challenging task. Methods based upon deep convolutional networks have consistently yield state of the art performance. Despite effective, of the drawbacks of these methods is their computational complexity, whereby testing and training of these networks require substantial computational resources and can be time consuming. Here we present a multi-scale kernel based on fully convolutional layers that is quite lightweight and can effectively segment large, medium, and thin vessels over a wide variations of contrast, position and size of the optic disk. Moreover, the architecture presented here makes use of these multi-scale kernels, reduced application of pooling operations and skip connections to achieve faster training. We illustrate the utility of our method for retinal vessel segmentation on the DRIVE, CHASE_DB and STARE data sets. We also compare the results delivered by our method with a number of alternatives elsewhere in the literature. In our experiments, our method always provides a margin of improvement on specificity, accuracy, AUC and sensitivity with respect to the alternative.
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Notes
- 1.
The dataset can found at https://blogs.kingston.ac.uk/retinal/chasedb1/.
- 2.
The dataset is widely available at https://drive.grand-challenge.org/.
- 3.
More information regarding the STARE project can be found at https://cecas.clemson.edu/~ahoover/stare/.
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Khan, T.M., Robles-Kelly, A., Naqvi, S.S., Arsalan, M. (2021). Residual Multiscale Full Convolutional Network (RM-FCN) for High Resolution Semantic Segmentation of Retinal Vasculature. In: Torsello, A., Rossi, L., Pelillo, M., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2021. Lecture Notes in Computer Science(), vol 12644. Springer, Cham. https://doi.org/10.1007/978-3-030-73973-7_31
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