DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation

Dong, Shunjie; Zhao, Jinlong; Zhang, Maojun; Shi, Zhengxue; Deng, Jianing; Shi, Yiyu; Mao, Tian; Zhuo, Cheng

doi:10.1007/978-3-030-59719-1_10

Cited by 19 publications

(7 citation statements)

References 11 publications

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“…It can raise the boundary integrity of ( 6) segmenting the brain tumor. The total loss function is illustrated in Equation (12).…”

Section: Boundary Lossmentioning

confidence: 99%

“…With the purpose of observing the spatial information in the 3D data, the 3D convolutional network was also applied to realize brain tumor segmentation. Dong et al [12] advised a three‐dimensional fully convolutional network according to the U‐Net to implement brain tumor segmentation. Although the segmentation performance can be improved by the 3D convolutional methods, the network parameters increased to a larger one, and it cost a lot of computing resources with low efficiency.…”

Section: Relevant Workmentioning

confidence: 99%

Section: Brain Tumor Segmentationmentioning

confidence: 99%

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Res‐MulFra: Multilevel and Multiscale Framework for Brain Tumor Segmentation

Huang,

Qiu,

Liu

et al. 2024

Int J Imaging Syst Tech

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In clinical diagnosis and surgical planning, extracting brain tumors from magnetic resonance images (MRI) is very important. Nevertheless, considering the high variability and imbalance of the brain tumor datasets, the way of designing a deep neural network for accurately segmenting the brain tumor still challenges the researchers. Moreover, as the number of convolutional layers increases, the deep feature maps cannot provide fine‐grained spatial information, and this feature information is useful for segmenting brain tumors from the MRI. Aiming to solve this problem, a brain tumor segmenting method of residual multilevel and multiscale framework (Res‐MulFra) is proposed in this article. In the proposed framework, the multilevel is realized by stacking the proposed RMFM‐based segmentation network (RMFMSegNet), which is mainly used to leverage the prior knowledge to gain a better brain tumor segmentation performance. The multiscale is implemented by the proposed RMFMSegNet, which includes both the parallel multibranch structure and the serial multibranch structure, and is mainly designed for obtaining the multiscale feature information. Moreover, from various receptive fields, a residual multiscale feature fusion module (RMFM) is also proposed to effectively combine the contextual feature information. Furthermore, in order to gain a better brain tumor segmentation performance, the channel attention module is also adopted. Through assessing the devised framework on the BraTS dataset and comparing it with other advanced methods, the effectiveness of the Res‐MulFra is verified by the extensive experimental results. For the BraTS2015 testing dataset, the Dice value of the proposed method is 0.85 for the complete area, 0.72 for the core area, and 0.62 for the enhanced area.

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“…It can raise the boundary integrity of ( 6) segmenting the brain tumor. The total loss function is illustrated in Equation (12).…”

Section: Boundary Lossmentioning

confidence: 99%

Section: Relevant Workmentioning

confidence: 99%

See 1 more Smart Citation

Res‐MulFra: Multilevel and Multiscale Framework for Brain Tumor Segmentation

Huang,

Qiu,

Liu

et al. 2024

Int J Imaging Syst Tech

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“…Zhao et al also utilized the U-Net architecture to segment cardiac chamber in magnetic resonance images [28]. A further study by Dong et al showed that U-Net could also be used for video segmentation for cardiac MRI video with state-of-the-art performance [29]. All those studies have proven that the deep learning approach was beneficial for cardiac chamber segmentation.…”

Section: Introductionmentioning

confidence: 99%

Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks

Saputra

Farhan

Suryanto

et al. 2022

Animals

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Water fleas are an important lower invertebrate model that are usually used for ecotoxicity studies. Contrary to mammals, the heart of a water flea has a single chamber, which is relatively big in size and with fast-beating properties. Previous cardiac chamber volume measurement methods are primarily based on ImageJ manual counting at systolic and diastolic phases which suffer from low efficiency, high variation, and tedious operation. This study provides an automated and robust pipeline for cardiac chamber size estimation by a deep learning approach. Image segmentation analysis was performed using U-Net and Mask RCNN convolutional networks on several different species of water fleas such as Moina sp., Daphnia magna, and Daphnia pulex. The results show that Mask RCNN performs better than U-Net at the segmentation of water fleas’ heart chamber in every parameter tested. The predictive model generated by Mask RCNN was further analyzed with the Cv2.fitEllipse function in OpenCV to perform a cardiac physiology assessment of Daphnia magna after challenging with the herbicide of Roundup. Significant increase in normalized stroke volume, cardiac output, and the shortening fraction was observed after Roundup exposure which suggests the possibility of heart chamber alteration after roundup exposure. Overall, the predictive Mask RCNN model established in this study provides a convenient and robust approach for cardiac chamber size and cardiac physiology measurement in water fleas for the first time. This innovative tool can offer many benefits to other research using water fleas for ecotoxicity studies.

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“…Ronneberger et al [5] proposed u-net, a semantic segmentation neural network which utilizes skip connection to inject low-level feature information to high-levesl feature, yielding a more delicate segmentation mask. In subsequent years, many approaches [6,7,8,9,10,11,12] have been proposed to deal with the inherent limitation of u-net. However, rare studies [13,14,15,16,17] have applied u-net structure to X-ray images.…”

Section: Introductionmentioning

confidence: 99%

Merged U-Net for Bone Tumors X-Ray Images Segmentation

Xie

Zhao

et al. 2022

2022 IEEE International Conference on Image Processing (ICIP)

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Bone tumors X-ray image segmentation is crucial for many medical image processing applications such as X-ray image enhancement, lesion diagnosis, etc. In this paper, we propose a new topological structure of u-net, named merged u-net, for bone tumors X-ray image segmentation. We attach a topdown merged branch to the vanilla encoder-decoder network, enhancing the hierarchical feature aggregation. In the merged path, a multi-feature aggregation block, named merged gate, is proposed for better localization of lesion area. According to the attention matrix, the proposed merged gate can generate a reconstructed feature that contains both low-level information and high-level information. Then we incorporate the reconstructed feature with the small scare feature cued with a local feature fusion method, achieving multi-scare feature aggregation. Additionally, we propose a new X-ray image dataset for bone tumors segmentation, which consists of 88 benign images and 217 malignant images, provided with corresponding segmentation masks. Experimental results demonstrate that the proposed merged u-net outperforms other u-net based medical segmentation methods on the proposed X-ray image dataset.

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DeU-Net: Deformable U-Net for 3D Cardiac MRI Video Segmentation

Cited by 19 publications

References 11 publications

Res‐MulFra: Multilevel and Multiscale Framework for Brain Tumor Segmentation

Res‐MulFra: Multilevel and Multiscale Framework for Brain Tumor Segmentation

Automated Cardiac Chamber Size and Cardiac Physiology Measurement in Water Fleas by U-Net and Mask RCNN Convolutional Networks

Merged U-Net for Bone Tumors X-Ray Images Segmentation

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