JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation

Wang, Yifan; Yan, Guoli; Zhu, Haikuan; Buch, Sagar; Wang, Ying; Haacke, E. Mark; Hua, Jing; Zhong, Zichun

doi:10.1007/978-3-030-59725-2_11

Cited by 20 publications

(16 citation statements)

References 25 publications

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“…In this section, we will show the results of the proposed method on three data sets and compare the Usception with the state‐of‐the‐art segmentation methods including V‐Net, 17 3D U‐Net, 15 3D U‐Net++, Uception, 16 and JointVesselNet 41 from both visual and quantitative perspectives. After that, we explore the influence of each module on segmentation results by conducting ablation study to prove the effectiveness of our method.…”

Section: Resultsmentioning

confidence: 99%

“…15 Recently, with the in-depth study of cerebrovascular disease, some new frameworks have been proposed for cerebrovascular segmentation. Wang et al 41 presented JointVesselNet for robust extraction of sparse vascular structure, where they utilized the maximum intensity projection (MIP) to obtain image composition and then embedded them into 3D MRA. Consequently, local vessels were enhanced and small vessels were better captured.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

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A nested parallel multiscale convolution for cerebrovascular segmentation

et al. 2021

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Purpose: Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular diseases. Many segmentation frameworks based on convolutional neural networks (CNNs) or U‐Net‐like structures have been proposed for cerebrovascular segmentation. Unfortunately, the segmentation results are still unsatisfactory, particularly in the small/thin cerebrovascular due to the following reasons: (1) the lack of attention to multiscale features in encoder caused by the convolutions with single kernel size; (2) insufficient extraction of shallow and deep‐seated features caused by the depth limitation of transmission path between encoder and decoder; (3) insufficient utilization of the extracted features in decoder caused by less attention to multiscale features. Methods: Inspired by U‐Net++, we propose a novel 3D U‐Net‐like framework termed Usception for small cerebrovascular. It includes three blocks: Reduction block, Gap block, and Deep block, aiming to: (1) improve feature extraction ability by grouping different convolution sizes; (2) increase the number of multiscale features in different layers by grouping paths of different depths between encoder and decoder; (3) maximize the ability of decoder in recovering multiscale features from Reduction and Gap block by using convolutions with different kernel sizes. Results: The proposed framework is evaluated on three public and in‐house clinical magnetic resonance angiography (MRA) data sets. The experimental results show that our framework reaches an average dice score of 69.29%, 87.40%, 77.77% on three data sets, which outperform existing state‐of‐the‐art methods. We also validate the effectiveness of each block through ablation experiments. Conclusions: By means of the combination of Inception‐ResNet and dimension‐expanded U‐Net++, the proposed framework has demonstrated its capability to maximize multiscale feature extraction, thus achieving competitive segmentation results for small cerebrovascular.

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Section: Resultsmentioning

confidence: 99%

Section: Deep Learning-based Methodsmentioning

confidence: 99%

A nested parallel multiscale convolution for cerebrovascular segmentation

et al. 2021

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“…Despite its simplicity, MIP has been widely adopted in medical image analysis, such as reconstruction [32,33], detection [34,35], and segmentation [36][37][38]. The paper proposing the JointVesselNet [23] applied the idea of MIP to cerebrovascular segmentation. Given a random extraction of a 3D patch of the size K 1 × K 2 × K 3, where K 3 is along the vertical axis, it computes s sliced (s = 6 in the paper) MIPs along the vertical axis.…”

Section: Multi-directional Mipmentioning

confidence: 99%

“…Based on the previous work, Yifan Wang et al proposed a multi-stream CNN framework JointVesselNet [23], which learns 3D features and the corresponding single-direction projection to obtain the 2D MIP features [24][25][26], and then integrates the two into the 3D space. For small blood vessels, better results were achieved.…”

Section: Introductionmentioning

confidence: 99%

Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs

Yong-wei

Kwak

2022

Applied Sciences

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The segmentation algorithm of cerebrovascular magnetic resonance angiography (MRA) images based on deep learning plays an essential role in medical study. Traditional segmentation algorithms face poor segmentation results and poor connectivity when the cerebrovascular vessels are thinner. An improved segmentation algorithm based on deep convolutional networks is proposed in this research. The proposed segmentation network combines the original 3D U-Net with the maximum intensity projection (MIP), which was transformed from the corresponding patch of a 3D MRA image. The MRA dataset provided by Jeonbuk National University Hospital was used to evaluate the experimental results in comparison with traditional 3D cerebrovascular segmentation methods and other state–of–the–art deep learning methods. The experimental results showed that our method achieved the best test performance among the compared methods in terms of the Dice score when Inception blocks and attention modules were placed in the proposed dual-path networks.

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“…The network removes artifacts caused by dilated convolution through residual connection and improves the accuracy of segmentation. Wang et al [ 40 , 41 ] extracted and visualized 3D cerebrovascular structures from highly sparse and noisy MRA images based on deep learning. The learned 2D multi-view slice feature vector is projected into 3D space to extract small blood vessels and improve vascular connectivity.…”

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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JointVesselNet: Joint Volume-Projection Convolutional Embedding Networks for 3D Cerebrovascular Segmentation

Cited by 20 publications

References 25 publications

A nested parallel multiscale convolution for cerebrovascular segmentation

A nested parallel multiscale convolution for cerebrovascular segmentation

Cerebrovascular Segmentation Model Based on Spatial Attention-Guided 3D Inception U-Net with Multi-Directional MIPs

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

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