Width Attention based Convolutional Neural Network for Retinal Vessel Segmentation

Alvarado-Carrillo, Dora E.; Dalmau, Oscar

doi:10.1016/j.eswa.2022.118313

Cited by 23 publications

(2 citation statements)

References 41 publications

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“…These results demonstrate that the proposed method works better on the DRIVE [40] and STARE [41] databases than any other method. The AUC of proposed method for CHASE_DB1 [42] database is slightly below AUCs of MRC NET [48], WA-Net [61], and Bridge-Net [62]. However, it is noteworthy that the F1 score, Accuracy, Sensitivity, and Specificity obtained by our method on the CHASE_DB1 database surpass those of MRC NET [48].…”

Section: ) Experiments 3: Comparative Analysis With Existing Methodsmentioning

confidence: 69%

MSR U-Net: An Improved U-Net Model for Retinal Blood Vessel Segmentation

Kande,

Ravi,

Kande

et al. 2024

IEEE Access

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For proper diagnosis and treatment of various retinal conditions, retinal blood vessel segmentation is crucial. Delineation of vessels with varying thicknesses is critical for detecting disease symptoms. However, this task is challenging due to inadequate contextual information, complex vessel morphology, and lesion confusion. Many recent works employed several variations of CNNs with U-Net as baseline model for segmenting blood vessels from the fundus images. However, the existing methods still lack in generalizing the vessels well enough, indicating scope for improvement in this challenging problem of vessel segmentation. We introduce a novel Multi-Scale Residual (MSR) U-Net model in this study replacing convolution block and skip connections with an improved Multi-Scale Residual (MSR) convolution block and Bottleneck residual paths (B-Res paths) respectively. Specifically, STARE, DRIVE, and CHASE_DB1 datasets of fundus images are used to validate the proposed segmentation method. Our experimental results consistently showcase better/comparable performances when compared with current approaches, achieving higher area under receiver operator characteristic (AUC), accuracy, and F1 score. in segmenting blood vessels of varying thicknesses, even in scenarios with diverse contextual information, the presence of coexisting lesions, and complex vessel morphologies.

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Section: ) Experiments 3: Comparative Analysis With Existing Methodsmentioning

confidence: 69%

MSR U-Net: An Improved U-Net Model for Retinal Blood Vessel Segmentation

Kande,

Ravi,

Kande

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…For a better comparison with these methods, these models are trained using the same experimental settings as in this paper. In addition, we compare the performance with UNet++ [27], Li et al proposed mothed [44], D-MNet [50], WA-Net [51], and TUnet-LBF [52]. Among them, GT U-Net modifies both the encoder and decoder to achieve good performance on tooth segmentation.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

DT-Net: Joint Dual-Input Transformer and CNN for Retinal Vessel Segmentation

Jia,

Ma,

Geng

et al. 2023

Computers, Materials &Amp; Continua

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Retinal vessel segmentation in fundus images plays an essential role in the screening, diagnosis, and treatment of many diseases. The acquired fundus images generally have the following problems: uneven illumination, high noise, and complex structure. It makes vessel segmentation very challenging. Previous methods of retinal vascular segmentation mainly use convolutional neural networks on U Network (U-Net) models, and they have many limitations and shortcomings, such as the loss of microvascular details at the end of the vessels. We address the limitations of convolution by introducing the transformer into retinal vessel segmentation. Therefore, we propose a hybrid method for retinal vessel segmentation based on modulated deformable convolution and the transformer, named DT-Net. Firstly, multi-scale image features are extracted by deformable convolution and multi-head selfattention (MHSA). Secondly, image information is recovered, and vessel morphology is refined by the proposed transformer decoder block. Finally, the local prediction results are obtained by the side output layer. The accuracy of the vessel segmentation is improved by the hybrid loss function. Experimental results show that our method obtains good segmentation performance on Specificity (SP), Sensitivity (SE), Accuracy (ACC), Curve (AUC), and F1-score on three publicly available fundus datasets such as DRIVE, STARE, and CHASE_DB1.

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