Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

Jiang, Yun; Yao, Huixia; Ma, Zeqi; Zhang, Jingyao

doi:10.3390/sym13101820

Cited by 5 publications

(3 citation statements)

References 44 publications

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“…Subsequently, the created masks were compared with the ground truth binary mask. The last step was the performance assessment of the proposed model and the comparison of its performance with other semantic segmentation models, where the most popular evaluation matrices were used, namely recall, accuracy, specificity, precision, intersection over union (IoU), and dice similarity coefficient (DSC) [27]. The following are the definitions of each evaluation index:…”

Section: Evaluation Metricsmentioning

confidence: 99%

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Kumar

Song

et al. 2023

Life

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Left ventricular segmentation is a vital and necessary procedure for assessing cardiac systolic and diastolic function, while echocardiography is an indispensable diagnostic technique that enables cardiac functionality assessment. However, manually labeling the left ventricular region on echocardiography images is time consuming and leads to observer bias. Recent research has demonstrated that deep learning has the capability to employ the segmentation process automatically. However, on the downside, it still ignores the contribution of all semantic information through the segmentation process. This study proposes a deep neural network architecture based on BiSeNet, named Bi-DCNet. This model comprises a spatial path and a context path, with the former responsible for spatial feature (low-level) acquisition and the latter responsible for contextual semantic feature (high-level) exploitation. Moreover, it incorporates feature extraction through the integration of dilated convolutions to achieve a larger receptive field to capture multi-scale information. The EchoNet-Dynamic dataset was utilized to assess the proposed model, and this is the first bilateral-structured network implemented on this large clinical video dataset for accomplishing the segmentation of the left ventricle. As demonstrated by the experimental outcomes, our method obtained 0.9228 and 0.8576 in DSC and IoU, respectively, proving the structure’s effectiveness.

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Section: Evaluation Metricsmentioning

confidence: 99%

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Kumar

Song

et al. 2023

Life

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“…To identify the blood vessels, a Gaussian matched filter can be used as the gray level profile of the vasculature as approached by a Gaussian shaped curve. The particulars of the matched filter can be found in [45]; a short explanation is set out below. The matched filter based on the Gaussian kernel function is described as follows:…”

Section: Matched Filtermentioning

confidence: 99%

“…Ooi et al recommended a vessel extraction approach, using a Canny edge detector [44]. Jiang et al suggested a bilateral network with scale attention for the segmentation of vessels [45]. Dash et al suggested a hybrid technique for the extraction of thin and thick vessels [46].…”

Section: Introductionmentioning

confidence: 99%

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

et al. 2022

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Fundus images have been established as an important factor in analyzing and recognizing many cardiovascular and ophthalmological diseases. Consequently, precise segmentation of blood using computer vision is vital in the recognition of ailments. Although clinicians have adopted computer-aided diagnostics (CAD) in day-to-day diagnosis, it is still quite difficult to conduct fully automated analysis based exclusively on information contained in fundus images. In fundus image applications, one of the methods for conducting an automatic analysis is to ascertain symmetry/asymmetry details from corresponding areas of the retina and investigate their association with positive clinical findings. In the field of diabetic retinopathy, matched filters have been shown to be an established technique for vessel extraction. However, there is reduced efficiency in matched filters due to noisy images. In this work, a joint model of a fast guided filter and a matched filter is suggested for enhancing abnormal retinal images containing low vessel contrasts. Extracting all information from an image correctly is one of the important factors in the process of image enhancement. A guided filter has an excellent property in edge-preserving, but still tends to suffer from halo artifacts near the edges. Fast guided filtering is a technique that subsamples the filtering input image and the guidance image and calculates the local linear coefficients for upsampling. In short, the proposed technique applies a fast guided filter and a matched filter for attaining improved performance measures for vessel extraction. The recommended technique was assessed on DRIVE and CHASE_DB1 datasets and achieved accuracies of 0.9613 and 0.960, respectively, both of which are higher than the accuracy of the original matched filter and other suggested vessel segmentation algorithms.

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Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation

Jiang

Yao

Tao

et al. 2021

Sensors

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Segmentation of retinal vessels is a critical step for the diagnosis of some fundus diseases. Methods: To further enhance the performance of vessel segmentation, we propose a method based on a gated skip-connection network with adaptive upsampling (GSAU-Net). In GSAU-Net, a novel skip-connection with gating is first utilized in the extension path, which facilitates the flow of information from the encoder to the decoder. Specifically, we used the gated skip-connection between the encoder and decoder to gate the lower-level information from the encoder. In the decoding phase, we used an adaptive upsampling to replace the bilinear interpolation, which recovers feature maps from the decoder to obtain the pixelwise prediction. Finally, we validated our method on the DRIVE, CHASE, and STARE datasets. Results: The experimental results showed that our proposed method outperformed some existing methods, such as DeepVessel, AG-Net, and IterNet, in terms of accuracy, F-measure, and AUCROC. The proposed method achieved a vessel segmentation F-measure of 83.13%, 81.40%, and 84.84% on the DRIVE, CHASE, and STARE datasets, respectively.

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Bi-SANet—Bilateral Network with Scale Attention for Retinal Vessel Segmentation

Cited by 5 publications

References 44 publications

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Bi-DCNet: Bilateral Network with Dilated Convolutions for Left Ventricle Segmentation

Guidance Image-Based Enhanced Matched Filter with Modified Thresholding for Blood Vessel Extraction

Gated Skip-Connection Network with Adaptive Upsampling for Retinal Vessel Segmentation

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