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 69 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)

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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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CAS‐GAN: A Novel Generative Adversarial Network‐Based Architecture for Coronary Artery Segmentation

Hamdi,

Kerkeni,

Abdallah

et al. 2024

Int J Imaging Syst Tech

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Accurate and automated segmentation of x‐ray coronary angiography (XRCA) is crucial for both diagnosing and treating coronary artery diseases. Despite the outstanding results achieved by deep learning (DL)‐based methods in this area, this task remains challenging due to several factors such as poor image quality, the presence of motion artifacts, and inherent variability in vessel structure sizes. To address this challenge, this paper introduces a novel GAN‐based architecture for coronary artery segmentation using XRCA images. This architecture includes a novel U‐Net variant with two types of self‐attention blocks in the generator segment. An auxiliary path connects the attention block and the prediction block to enhance feature generalization, improving vessel structure delineation, especially thin vessels in low‐contrast regions. In parallel, the discriminator network employs a residual CNN with similar attention blocks for balanced performance and improved predictive capabilities. With a streamlined 6.74 M parameters, the resulting architecture surpasses existing methods in efficiency. We assess its efficacy on three coronary artery datasets: our private “CORONAR,” and the public “DCA1” and “CHUAC” datasets. Empirical results showcase our model's superiority across these datasets, utilizing both original and preprocessed images. Notably, our proposed architecture achieves the highest F1‐score of 0.7972 for the CHUAC dataset, 0.8245 for the DCA1 dataset, and 0.8333 for the CORONAR dataset.

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

Cited by 69 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

CAS‐GAN: A Novel Generative Adversarial Network‐Based Architecture for Coronary Artery Segmentation

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