Modified Depthwise Parallel Attention UNet for Retinal Vessel Segmentation

Abstract

Retinal fundus images contain highly informative geometrical features for detecting diabetic retinopathy (DR), including vessels, especially thin and low-contrast vessels, which are predominant features for accurately diagnosing diabetic retinopathy. Automatic segmentation methods have been developed based on deep convolutional neural networks to replace manual labeling. These methods have shown acceptable performance in fundus vessel segmentation. The UNet model is a well-known architecture of deep neural networks often used for vessel segmentation tasks and has achieved significant performance. However, segmentation tasks remain challenging due to multiple convolutions, down-sampling operations, and inadequate feature fusion in the encoder-decoder architecture. Also, traditional convolution increases the number of multiplications while performing convolution operations. These challenges lead to the loss of information related to thin and low-contrast vessels, eventually affecting the segmentation performance. To tackle this issue, we propose incorporating depthwise parallel attention in the existing UNet framework (DPA-UNet) to achieve accurate vessel segmentation. This approach entails the integration of a depthwise convolution block in the downsampling path and a parallel attention mechanism in the upsampling path of UNet. The primary benefit of depthwise convolution and global information embedding (GIE) is the ability to capture intricate information characteristics across channels. This helps to minimize the information degradation caused by conventional convolution and downsampling techniques. A parallel attention network is proposed in the upsampling path of the existing UNet to optimize the channel and spatial information acquired from the encoder-decoder. Extensive experiments are conducted on three publicly available datasets, namely DRIVE, STARE, and CHASE_DB1, to validate the performance of the proposed model. The findings indicate that the UNET model with depthwise parallel attention achieved a competitive performance with fewer network parameters in segmenting retinal vessels.

ERBVS: Enhanced Retinal Blood Vessel Segmentation using Multiple Modalities and Attention Mechanisms with Adversarial Training and Ensemble Deep Learning Operations

“…This sets forth the critical image quality in the effectiveness of deep learning models. Radha and Karuna [37] presented a depthwise parallel attention UNet for segmentation of the retinal vessels, which proved the e ciency of attention mechanisms in enhancing the accuracy of segmentation models. The innovation is one more step to develop deep learning models speci ed for medical imaging.…”

TD Swin-UNet: Texture-Driven Swin-UNet with Enhanced Boundary-Wise Perception for Retinal Vessel Segmentation

“…They replaced the 3 × 3 convolution block with a Multiconv block to facilitate feature extraction across varying vessel thicknesses and finenesses, and also added residual convolution to the skip connection, reducing the information difference between the encoder and decoder. Radha et al [22] augmented the encoding process with a deepwise convolution block to mitigate information decay during downsampling and introduced a parallel attention network in the upsampling process to optimize the model structure. Cao et al [23] optimized the skip connection mechanism of U-Net, incorporating a Multi-scale Fusion Self-Attention Module to leverage diverse image scales.…”