Influence of background preprocessing on the performance of deep learning retinal vessel detection

Purpose: Segmentation of the vessel tree from retinal fundus images can be used to track changes in the retina, and be an important first step in a diagnosis. Manual segmentation is a time consuming process that is prone to error; effective and reliable automation can alleviate these problems but one of the difficulties is uneven image background which may affect segmentation performance.

Approach: We present a patch-based deep learning framework, based on a modified U-Net architecture, that automatically segments the retinal blood vessels from fundus images. In particular, we evaluate how various preprocessing techniques: images with either no processing, N4 bias field correction, contrast limited adaptive histogram equalization (CLAHE), or a combination of N4 and CLAHE, can compensate for uneven image background and impact final segmentation performance.

Results: We achieved competitive results on three publicly available datasets as a benchmark for our comparisons of pre-processing techniques. In addition, we introduce Bayesian statistical testing which indicates little practical difference (Pr > 0.99) between pre-processing methods apart from the sensitivity metric. In terms of sensitivity and pre-processing, the combination of N4 correction and CLAHE performs better in comparison to unprocessed and N4 pre-processing (Pr > 0.87) but compared to CLAHE alone, the differences are not significant (Pr ≈ 0.38 − 0.88).

Conclusions: We conclude that deep learning is an effective method for retinal vessel segmentation and that CLAHE pre-processing has the greatest positive impact on segmentation performance, with N4 correction helping only in images with extremely inhomogeneous background illumination.