Scale Space Approximation in Convolutional Neural Networks for Retinal Vessel Segmentation

TL;DR

This paper introduces a novel multi-scale residual CNN with a scale-space approximation block, closely mimicking Gaussian filtering, to enhance retinal vessel segmentation, outperforming existing methods.

Contribution

The paper presents a new multi-scale residual CNN architecture utilizing SSA blocks that approximate Gaussian filtering for improved vessel segmentation.

Findings

Outperforms current state-of-the-art methods

SSA blocks significantly improve segmentation accuracy

Frequency domain analysis confirms SSA as a Gaussian filter approximation

Abstract

Retinal images have the highest resolution and clarity among medical images. Thus, vessel analysis in retinal images may facilitate early diagnosis and treatment of many chronic diseases. In this paper, we propose a novel multi-scale residual convolutional neural network structure based on a \emph{scale-space approximation (SSA)} block of layers, comprising subsampling and subsequent upsampling, for multi-scale representation. Through analysis in the frequency domain, we show that this block structure is a close approximation of Gaussian filtering, the operation to achieve scale variations in scale-space theory. Experimental evaluations demonstrate that the proposed network outperforms current state-of-the-art methods. Ablative analysis shows that the SSA is indeed an important factor in performance improvement.