Automatic Segmentation of Cerebrovascular Based on Deep Learning

Min, Yuqin; Nie, Shengdong

doi:10.1145/3495018.3495035

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…Hilbert et al [ 8 ] proposed a high-performance, fully automatic segmentation framework BRAVE-NET, combining deep supervised networks and aggregating rough and low-resolution feature maps into the final convolution layer, effectively fusing multi-scale features. Min et al [ 34 ] introduced multi-scale inputs and residual mechanisms into the U-Net network to improve the model’s performance while maintaining generalization ability. Oktay et al [ 17 ] introduced a novel module known as the Self-Attention Gate module, which enhances the significance of local regions and improves the model’s sensitivity to the foreground, ultimately enhancing segmentation accuracy.…”

Section: Related Workmentioning

confidence: 99%

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

et al. 2023

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Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and pathological detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse attention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edged features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features’ expression ability without increasing the model’s complexity. Finally, the proposed training sample cropping strategy reduces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation techniques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway structures. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare professionals.

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Section: Related Workmentioning

confidence: 99%

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

et al. 2023

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“…With the highest DSC, the MDNet-Vb model surpasses Resnet, DenseNet, 3D U-net, V-net, and DeepMedic (72.91% and 69.32% consecutively on CTA and MRA datasets). Using multiscale inputs and residuals, Min et al [81] proposed a modification to the U-net architecture. Inspired by [77], the proposed method added two 1x1x1 convolution layers on the final level to achieve a fully connected layer.…”

Section: ) U-net Model and Its Modificationsmentioning

confidence: 99%

Brain Vessel Segmentation Using Deep Learning—A Review

et al. 2022

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This article provides a comprehensive review of deep learning-based blood vessel segmentation of brain. The cerebrovascular disease develops when blood arteries in the brain are compromised, resulting in severe brain injuries such as ischemic stroke, brain hemorrhages, and many more. Early detection enables patients to obtain more effective treatment before becoming critically unwell. Due to the superior efficiency and accuracy compared to manual segmentation and other computer-assisted diagnosis procedures, deep learning algorithms have been extensively deployed in brain vascular segmentation. This study examined current articles on deep learning-based brain vascular segmentation, which examined the proposed methodologies, particularly the network architectures, and determined the model trend. We evaluated challenges and crucial factors associated with the application of deep learning to brain vascular segmentation, as well as future research prospects. This paper will assist researchers in developing more sophisticated and robust models in future to develop deep learning solution.

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Automatic Segmentation of Cerebrovascular Based on Deep Learning

Cited by 2 publications

References 7 publications

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

Tubular Structure Segmentation via Multi-Scale Reverse Attention Sparse Convolution

Brain Vessel Segmentation Using Deep Learning—A Review

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