A comprehensive survey on segmentation techniques for retinal vessel segmentation

Cervantes, Jair; Cervantes, Jared; García-Lamont, Farid; Yee-Rendón, Arturo; Cabrera, Javier; Jalili, Laura D.

doi:10.1016/j.neucom.2023.126626

Cited by 9 publications

(3 citation statements)

References 191 publications

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“…The MFMM merges the feature maps obtained at each stage of the decoder so that the final feature maps contain more information about the blood vessels, which, in turn, improves the final blood vessel segmentation accuracy and effectiveness. The MFMM realization step is shown in Equations ( 5) and (6). In this module, the feature maps of each scale are denoted as F x , and x denotes each stage of the decoder.…”

Section: Multi-scale Feature Merging Modulementioning

confidence: 99%

“…Current medical research suggests that retinal vasculopathy may precede cardiovascular diseases, such as hypertension, coronary artery disease, and diabetes, and that retinal vessel segmentation can be used as a basis for diagnosing related diseases [1][2][3][4][5]. However, the complex distribution and trend of blood vessels in the retina, the large variation in size and the interference of lesions, as well as the low illumination and imaging resolution of fundus cameras, make it difficult to completely segment retinal vessels [6]. Therefore, retinal vessel image segmentation has been a hot and difficult issue in the field of retinal image analysis.…”

Section: Introductionmentioning

confidence: 99%

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PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

Wang,

Wu,

Jia

2024

Applied Sciences

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Retinal vessel segmentation is critical for diagnosing related diseases in the medical field. However, the complex structure and variable size and shape of retinal vessels make segmentation challenging. To enhance feature extraction capabilities in existing algorithms, we propose PAM-UNet, a U-shaped network architecture incorporating a novel Plenary Attention Mechanism (PAM). In the BottleNeck stage of the network, PAM identifies key channels and embeds positional information, allowing spatial features within significant channels to receive more focus. We also propose a new regularization method, DropBlock_Diagonal, which discards diagonal regions of the feature map to prevent overfitting and enhance vessel feature learning. Within the decoder stage of the network, features from each stage are merged to enhance the segmentation accuracy of the final vessel. Experimental validation on two retinal image datasets, DRIVE and CHASE_DB1, shows that PAM-UNet achieves 97.15%, 83.16%, 98.45%, 83.15%, 98.66% and 97.64%, 85.82%, 98.46%, 82.56%, 98.95% on Acc, Se, Sp, F1, AUC, respectively, outperforming UNet and most other retinal vessel segmentation algorithms.

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Section: Multi-scale Feature Merging Modulementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

Wang,

Wu,

Jia

2024

Applied Sciences

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“…Firstly, to enhance the global context information of brain tumours and expand the receptive field, a lightweight non-local EMA module ( Cervantes et al, 2023 ) is introduced into the cascaded network. Additionally, to address the difficulty of capturing tumour features of different shapes and sizes effectively with regular convolutions, an attention-based dynamic convolution ( Dutande et al, 2021 ) is attempted in the network.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Byeon,

Al-Kubaisi,

Dutta

et al. 2024

Front. Comput. Neurosci.

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According to experts in neurology, brain tumours pose a serious risk to human health. The clinical identification and treatment of brain tumours rely heavily on accurate segmentation. The varied sizes, forms, and locations of brain tumours make accurate automated segmentation a formidable obstacle in the field of neuroscience. U-Net, with its computational intelligence and concise design, has lately been the go-to model for fixing medical picture segmentation issues. Problems with restricted local receptive fields, lost spatial information, and inadequate contextual information are still plaguing artificial intelligence. A convolutional neural network (CNN) and a Mel-spectrogram are the basis of this cough recognition technique. First, we combine the voice in a variety of intricate settings and improve the audio data. After that, we preprocess the data to make sure its length is consistent and create a Mel-spectrogram out of it. A novel model for brain tumor segmentation (BTS), Intelligence Cascade U-Net (ICU-Net), is proposed to address these issues. It is built on dynamic convolution and uses a non-local attention mechanism. In order to reconstruct more detailed spatial information on brain tumours, the principal design is a two-stage cascade of 3DU-Net. The paper’s objective is to identify the best learnable parameters that will maximize the likelihood of the data. After the network’s ability to gather long-distance dependencies for AI, Expectation–Maximization is applied to the cascade network’s lateral connections, enabling it to leverage contextual data more effectively. Lastly, to enhance the network’s ability to capture local characteristics, dynamic convolutions with local adaptive capabilities are used in place of the cascade network’s standard convolutions. We compared our results to those of other typical methods and ran extensive testing utilising the publicly available BraTS 2019/2020 datasets. The suggested method performs well on tasks involving BTS, according to the experimental data. The Dice scores for tumor core (TC), complete tumor, and enhanced tumor segmentation BraTS 2019/2020 validation sets are 0.897/0.903, 0.826/0.828, and 0.781/0.786, respectively, indicating high performance in BTS.

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Multi-scale Knowledge Transfer Vision Transformer for 3D vessel shape segmentation

Hua,

Wu,

Zhong

2024

Computers & Graphics

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A comprehensive survey on segmentation techniques for retinal vessel segmentation

Cited by 9 publications

References 191 publications

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism

Brain tumor segmentation using neuro-technology enabled intelligence-cascaded U-Net model

Multi-scale Knowledge Transfer Vision Transformer for 3D vessel shape segmentation

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