Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation

Li, Wentao; Yang, Huihua; Tian, Tong; Cao, Zhiwei; Pan, Xipeng; Xu, Weijin; Yang, Jiazhong; Gao, Feng

doi:10.1109/jbhi.2022.3188710

Cited by 91 publications

(27 citation statements)

References 31 publications

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“…The FR-UNet network, which was originally proposed for the detection of blood vessels in color fundus photographs, obtains the highest values for selected metrics in the case of OCT-reconstructed fundus. For this neural network, the impact of the dual-threshold iterative (DTI) algorithm [ 17 ] was also tested. The accuracy, sensitivity, F1 score, and AUC for FR-UNet are the highest (for the P3 reconstruction).…”

Section: Resultsmentioning

confidence: 99%

“…The fifth neural network model considered in our application is a new approach called Full-Resolution and Dual-Threshold Iteration based on the UNet architecture [ 17 ]. It extends the original approach by horizontal and vertical expansion through a multiresolution convolution interactive mechanism.…”

Section: Methodsmentioning

confidence: 99%

“…A typical approach is to take the threshold as 0.5. As in the case of fundus image analysis [ 17 ], a dual-threshold iterative algorithm (DTI) can be used, which results from taking into account the surroundings of a given pixel. The threshold can also be performed using different techniques [ 18 ], e.g., slope difference distribution (SDD) clustering and threshold selection, which have been proposed for magnetic resonance (MR) images and is effective for the detection of ventricle ROI.…”

Section: Introductionmentioning

confidence: 99%

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Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

Marciniak

Stankiewicz

Zaradzki

2023

Sensors

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The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

Marciniak

Stankiewicz

Zaradzki

2023

Sensors

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“…We use the first annotation as the ground truth. Following [22], we use the first 20 images for training, and the remaining 8 images for testing.…”

Section: Dice Mioumentioning

confidence: 99%

“…Results (%) of Retinal Vessel Segmentation on CHASE DB1 dataset. The results of UNet, UNet++, Attention UNet, and FR-UNet are taken from[22]. All other results are averaged over five runs in our experimental setups.…”

mentioning

confidence: 99%

Medical Image Segmentation via Cascaded Attention Decoding

Rahman

Mărculescu

2023

2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

100

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Transformers have shown great success in medical image segmentation. However, transformers may exhibit a limited generalization ability due to the underlying single-scale selfattention (SA) mechanism. In this paper, we address this issue by introducing a Multiscale hiERarchical vIsion Transformer (MERIT) backbone network, which improves the generalizability of the model by computing SA at multiple scales. We also incorporate an attention-based decoder, namely Cascaded Attention Decoding (CASCADE), for further refinement of multi-stage features generated by MERIT. Finally, we introduce an effective multi-stage feature mixing loss aggregation (MUTATION) method for better model training via implicit ensembling. Our experiments on two widely used medical image segmentation benchmarks (i.e., Synapse Multi-organ, ACDC) demonstrate the superior performance of MERIT over state-of-the-art methods. Our MERIT architecture and MUTATION loss aggregation can be used with downstream medical image and semantic segmentation tasks.

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Unsupervised Domain Adaptation for Simultaneous Segmentation and Classification of the Retinal Arteries and Veins

Xue,

Zhang,

et al. 2024

Int J Imaging Syst Tech

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Automatic segmentation of the fundus retinal vessels and accurate classification of the arterial and venous vessels play an important role in clinical diagnosis. This article proposes a fundus retinal vascular segmentation and arteriovenous classification network that combines the adversarial training and attention mechanism to address the issues of fundus retinal arteriovenous classification error and ambiguous segmentation of fine blood vessels. It consists of three core components: discriminator, generator, and segmenter. In order to address the domain shift issue, U‐Net is employed as a discriminator, and data samples for arterial and venous vessels are generated with a generator using an unsupervised domain adaption (UDA) approach. The classification of retinal arterial and venous vessels (A/V) as well as the segmentation of fine vessels is improved by adding a self‐attention mechanism to improve attention to vessel edge features and the terminal fine vessels. Non‐strided convolution and non‐pooled downsampling methods are also used to avoid losing fine‐grained information and learning less effective feature representations. The performance of multi‐class blood vessel segmentation is as follows, per test results on the DRIVE dataset: F1‐score (F1) has a value of 0.7496 and an accuracy of 0.9820. The accuracy of A/V categorization has increased by 1.35% when compared to AU‐Net. The outcomes demonstrate that by enhancing the baseline U‐Net, the strategy we suggested enhances the automated classification and segmentation of blood vessels.

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Full-Resolution Network and Dual-Threshold Iteration for Retinal Vessel and Coronary Angiograph Segmentation

Cited by 91 publications

References 31 publications

Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction

Medical Image Segmentation via Cascaded Attention Decoding

Unsupervised Domain Adaptation for Simultaneous Segmentation and Classification of the Retinal Arteries and Veins

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