Multi-task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

Ma, Wenao; Yu, Shuang; Ma, Kai; Wang, Jiexiang; Ding, Xinghao; Zheng, Yefeng

doi:10.1007/978-3-030-32239-7_85

Cited by 64 publications

(62 citation statements)

References 19 publications

(22 reference statements)

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“…Evaluation under the same criteria as used by known studies reveals that VTG-Net achieves an mean accuracy We should mention that a main limitation of our framework evaluation is that the sizes of the datasets used are relatively small. For the public AV-DRIVE dataset, we have followed the standard training/test splitting adopted in existing work [e.g., (12,13)] in the evaluation, and our VTG-Net has outperformed those methods on this dataset. In our future work, we plan to conduct training and validation of VTG-Net on larger datasets for a more thorough evaluation.…”

Section: Resultsmentioning

confidence: 99%

“…A SegNet ( 10 ) inspired encoder-decoder architecture ( 11 ) was proposed for pixel-wise classification. A multi-task framework with spatial activation was given ( 12 ) for simultaneous vessel segmentation and classification. Although outperforming traditional graph based methods, CNN approaches still suffer several drawbacks: (i) limited vessel connectivity; (ii) multiple class assignment of a single vessel segment.…”

Section: Introductionmentioning

confidence: 99%

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VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

et al. 2021

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From diagnosing cardiovascular diseases to analyzing the progression of diabetic retinopathy, accurate retinal artery/vein (A/V) classification is critical. Promising approaches for A/V classification, ranging from conventional graph based methods to recent convolutional neural network (CNN) based models, have been known. However, the inability of traditional graph based methods to utilize deep hierarchical features extracted by CNNs and the limitations of current CNN based methods to incorporate vessel topology information hinder their effectiveness. In this paper, we propose a new CNN based framework, VTG-Net (vessel topology graph network), for retinal A/V classification by incorporating vessel topology information. VTG-Net exploits retinal vessel topology along with CNN features to improve A/V classification accuracy. Specifically, we transform vessel features extracted by CNN in the image domain into a graph representation preserving the vessel topology. Then by exploiting a graph convolutional network (GCN), we enable our model to learn both CNN features and vessel topological features simultaneously. The final predication is attained by fusing the CNN and GCN outputs. Using a publicly available AV-DRIVE dataset and an in-house dataset, we verify the high performance of our VTG-Net for retinal A/V classification over state-of-the-art methods (with ~2% improvement in accuracy on the AV-DRIVE dataset).

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

et al. 2021

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“…We adopt average accuracy (Acc), sensitivity (Sen), specificity (Sp) and F1-score (F1) for quantitative evaluation. Besides, following [4], we evaluate A/V classification on the…”

Section: Resultsmentioning

confidence: 99%

“…Target-only in D→I is lacking since INSPIRE has only centerline annotations that can't be used for supervised learning. Please note that here we evaluate A/V-classification performance on the ground truth vessels pixels, which is a more strict criterion, following [4,5]. denotes the observed negative transfer.…”

Section: Ground Truthmentioning

confidence: 99%

Consistent Posterior Distributions Under Vessel-Mixing: A Regularization For Cross-Domain Retinal Artery/Vein Classification

Zhang

Liang

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

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Retinal artery/vein (A/V) classification is a critical technique for diagnosing diabetes and cardiovascular diseases. Although deep learning based methods achieve impressive results in A/V classification, the performance usually degrades when directly apply the models that trained on one dataset to another set, due to the domain shift, e.g., caused by the variations in imaging protocols. In this paper, we propose a novel method to improve cross-domain generalization for pixel-wise retinal A/V classification. That is, vessel-mixing based consistency regularization, which regularizes the models to give consistent posterior distributions for vessel-mixing samples. The proposed method achieves the state-of-the-art performance on extensive experiments for cross-domain A/V classification, which is even close to the performance of fully supervised learning on target domain in some cases.

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“…Hemelings et al (2019) proposed a novel FCN-based U-Net architecture for simultaneous blood vessel semantic segmentation and A/V discrimination. Ma et al (2019) proposed an enhanced deep architecture with a spatial activation mechanism for joint vessel segmentation and A/V identification. Li et al (2020) made a highly confident prediction about the peripheral vessels by taking the structural information among vessels into account with post-processing.…”

Section: Introductionmentioning

confidence: 99%

Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images

Wang

Cao

et al. 2021

Front. Cell Dev. Biol.

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Retinal blood vessel morphological abnormalities are generally associated with cardiovascular, cerebrovascular, and systemic diseases, automatic artery/vein (A/V) classification is particularly important for medical image analysis and clinical decision making. However, the current method still has some limitations in A/V classification, especially the blood vessel edge and end error problems caused by the single scale and the blurred boundary of the A/V. To alleviate these problems, in this work, we propose a vessel-constraint network (VC-Net) that utilizes the information of vessel distribution and edge to enhance A/V classification, which is a high-precision A/V classification model based on data fusion. Particularly, the VC-Net introduces a vessel-constraint (VC) module that combines local and global vessel information to generate a weight map to constrain the A/V features, which suppresses the background-prone features and enhances the edge and end features of blood vessels. In addition, the VC-Net employs a multiscale feature (MSF) module to extract blood vessel information with different scales to improve the feature extraction capability and robustness of the model. And the VC-Net can get vessel segmentation results simultaneously. The proposed method is tested on publicly available fundus image datasets with different scales, namely, DRIVE, LES, and HRF, and validated on two newly created multicenter datasets: Tongren and Kailuan. We achieve a balance accuracy of 0.9554 and F1 scores of 0.7616 and 0.7971 for the arteries and veins, respectively, on the DRIVE dataset. The experimental results prove that the proposed model achieves competitive performance in A/V classification and vessel segmentation tasks compared with state-of-the-art methods. Finally, we test the Kailuan dataset with other trained fusion datasets, the results also show good robustness. To promote research in this area, the Tongren dataset and source code will be made publicly available. The dataset and code will be made available at https://github.com/huawang123/VC-Net.

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Multi-task Neural Networks with Spatial Activation for Retinal Vessel Segmentation and Artery/Vein Classification

Cited by 64 publications

References 19 publications

VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification

Consistent Posterior Distributions Under Vessel-Mixing: A Regularization For Cross-Domain Retinal Artery/Vein Classification

Automatic Artery/Vein Classification Using a Vessel-Constraint Network for Multicenter Fundus Images

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