Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network

Zhang, Hao; Xia, Likun; Song, Ran; Yang, Jianlong; Hao, Honggang; Liu, Jiang; Zhao, Yitian

doi:10.1007/978-3-030-59725-2_7

Cited by 26 publications

(16 citation statements)

References 25 publications

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“…The learned 2D multi-view slice feature vector is projected into 3D space to extract small blood vessels and improve vascular connectivity. Zhang et al [ 42 ] introduced the reverse edge attention network to find missing cerebrovascular edge features and details, and, furthermore, improved the segmentation effect of small blood vessels. Nazir et al [ 43 ] proposed an efficient fusion network for automatic segmentation of cerebral vessels from CTA images and used residual mapping to solve the problems of network convergence.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes

Bai

Liu

Jiang

et al. 2022

Cells

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Automatic extraction of cerebral vessels and cranial nerves has important clinical value in the treatment of trigeminal neuralgia (TGN) and hemifacial spasm (HFS). However, because of the great similarity between different cerebral vessels and between different cranial nerves, it is challenging to segment cerebral vessels and cranial nerves in real time on the basis of true-color microvascular decompression (MVD) images. In this paper, we propose a lightweight, fast semantic segmentation Microvascular Decompression Network (MVDNet) for MVD scenarios which achieves a good trade-off between segmentation accuracy and speed. Specifically, we designed a Light Asymmetric Bottleneck (LAB) module in the encoder to encode context features. A Feature Fusion Module (FFM) was introduced into the decoder to effectively combine high-level semantic features and underlying spatial details. The proposed network has no pretrained model, fewer parameters, and a fast inference speed. Specifically, MVDNet achieved 76.59% mIoU on the MVD test set, has 0.72 M parameters, and has a 137 FPS speed using a single GTX 2080Ti card.

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Section: Related Workmentioning

confidence: 99%

Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes

Bai

Liu

Jiang

et al. 2022

Cells

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“…Several cerebrovascular segmentation methods inspired by the human attention mechanism have also been developed. Zhang et al (2020) proposed a convolutional neural network based on reverse edge attention mechanism (RE-Net) to perform 3D cerebrovascular segmentation and surface reconstruction. To capture detailed information of brain vessels at different resolutions, Ni et al (2020) proposed a global channel attention model for brain vessel segmentation.…”

Section: D Medical Image Segmentationmentioning

confidence: 99%

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning

et al. 2021

Self Cite

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Trigeminal neuralgia caused by paroxysmal and severe pain in the distribution of the trigeminal nerve is a rare chronic pain disorder. It is generally accepted that compression of the trigeminal root entry zone by vascular structures is the major cause of primary trigeminal neuralgia, and vascular decompression is the prior choice in neurosurgical treatment. Therefore, accurate preoperative modeling/segmentation/visualization of trigeminal nerve and its surrounding cerebrovascular is important to surgical planning. In this paper, we propose an automated method to segment trigeminal nerve and its surrounding cerebrovascular in the root entry zone, and to further reconstruct and visual these anatomical structures in three-dimensional (3D) Magnetic Resonance Angiography (MRA). The proposed method contains a two-stage neural network. Firstly, a preliminary confidence map of different anatomical structures is produced by a coarse segmentation stage. Secondly, a refinement segmentation stage is proposed to refine and optimize the coarse segmentation map. To model the spatial and morphological relationship between trigeminal nerve and cerebrovascular structures, the proposed network detects the trigeminal nerve, cerebrovasculature, and brainstem simultaneously. The method has been evaluated on a dataset including 50 MRA volumes, and the experimental results show the state-of-the-art performance of the proposed method with an average Dice similarity coefficient, Hausdorff distance, and average surface distance error of 0.8645, 0.2414, and 0.4296 on multi-tissue segmentation, respectively.

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“…Ma et al [44] introduced a novel GAN-based model, i.e., CSI-GAN, for medical image enhancement, where illumination regularisation and structure loss were used as constraints of training. Zhang et al [45] combined a Retinex model with the reverse edge attention network for cerebrovascular segmentation. The utilisation of reverse edge attention module significantly improved the performance of segmentation.…”

Section: Introductionmentioning

confidence: 99%

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information

Huang

Ding

et al. 2022

Appl Intell

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In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing.

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Cerebrovascular Segmentation in MRA via Reverse Edge Attention Network

Cited by 26 publications

References 25 publications

Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes

Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes

Automated Segmentation of Trigeminal Nerve and Cerebrovasculature in MR-Angiography Images by Deep Learning

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information

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