MPG-Net: Multi-Prediction Guided Network for Segmentation of Retinal Layers in OCT Images

TL;DR

MPG-Net is a novel multi-prediction guided attention network that enhances retinal layer segmentation in OCT images by refining features and providing semantic guidance, leading to improved accuracy over existing methods.

Contribution

The paper introduces a multi-prediction guided attention mechanism and a feature refinement module to improve retinal layer segmentation in OCT images.

Findings

Outperforms state-of-the-art methods on Duke OCT dataset

Improves segmentation accuracy through semantic-guided attention

Enhances feature discrimination with adaptive re-weighting

Abstract

Optical coherence tomography (OCT) is a commonly-used method of extracting high resolution retinal information. Moreover there is an increasing demand for the automated retinal layer segmentation which facilitates the retinal disease diagnosis. In this paper, we propose a novel multiprediction guided attention network (MPG-Net) for automated retinal layer segmentation in OCT images. The proposed method consists of two major steps to strengthen the discriminative power of a U-shape Fully convolutional network (FCN) for reliable automated segmentation. Firstly, the feature refinement module which adaptively re-weights the feature channels is exploited in the encoder to capture more informative features and discard information in irrelevant regions. Furthermore, we propose a multi-prediction guided attention mechanism which provides pixel-wise semantic prediction guidance to better recover the segmentation mask at each scale. This mechanism which transforms the deep supervision to supervised attention is able to guide feature aggregation with more semantic information between intermediate layers. Experiments on the publicly available Duke OCT dataset confirm the effectiveness of the proposed method as well as an improved performance over other state-of-the-art approaches.