RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Wang, Jingjing; Yu, Zishu; Luan, Zhenye; Ren, Jinwen; Yan-hua, Zhao; Yu, Gang

doi:10.3389/fonc.2022.805263

Cited by 14 publications

(10 citation statements)

References 44 publications

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“…Figure 2a illustrates the CBAM structure, Figure 2b shows the channel attention, and Figure 2c indicates the spatial attention. A CBAM with channel attention and spatial attention facilitates the combination of more expressive feature information, thereby leading to a more efficient extraction of contextual information from images of various scales [24]. In our model, each decoder layer gets the feature map F ∈ R C×H×W fed into a convolution operation, and then this feature map F is considered the input feature map of CBAM.…”

Section: Cbam In the Decodermentioning

confidence: 99%

A Medical Image Segmentation Method Based on Improved UNet 3+ Network

Hou

Wang

et al. 2023

Diagnostics

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In recent years, segmentation details and computing efficiency have become more important in medical image segmentation for clinical applications. In deep learning, UNet based on a convolutional neural network is one of the most commonly used models. UNet 3+ was designed as a modified UNet by adopting the architecture of full-scale skip connections. However, full-scale feature fusion can result in excessively redundant computations. This study aimed to reduce the network parameters of UNet 3+ while further improving the feature extraction capability. First, to eliminate redundancy and improve computational efficiency, we prune the full-scale skip connections of UNet 3+. In addition, we use the attention module called Convolutional Block Attention Module (CBAM) to capture more essential features and thus improve the feature expression capabilities. The performance of the proposed model was validated by three different types of datasets: skin cancer segmentation, breast cancer segmentation, and lung segmentation. The parameters are reduced by about 36% and 18% compared to UNet and UNet 3+, respectively. The results show that the proposed method not only outperformed the comparison models in a variety of evaluation metrics but also achieved more accurate segmentation results. The proposed models have lower network parameters that enhance feature extraction and improve segmentation performance efficiently. Furthermore, the models have great potential for application in medical imaging computer-aided diagnosis.

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Section: Cbam In the Decodermentioning

confidence: 99%

A Medical Image Segmentation Method Based on Improved UNet 3+ Network

Hou

Wang

et al. 2023

Diagnostics

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“…It designs a 3D dilated convolution integral-layer feature pyramid and adds it to the end of the backbone network, which further improves the segmentation accuracy of enhanced tumor and tumor core by combining with the contextual features, but it cannot extract brain tumors with very complex boundaries well. RDAU-Net [ 31 ] adds an extended feature pyramid module with an attention mechanism between the encoder and decoder, effectively obtaining feature maps of various sizes through different dilated convolutions while extracting useful information about channels and spaces. It solves the problem of traditional U-Net networks being unable to extract the multi-scale features of images.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation Network with Multi-View Ensemble Discrimination and Kernel-Sharing Dilated Convolution

et al. 2023

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Accurate segmentation of brain tumors from magnetic resonance 3D images (MRI) is critical for clinical decisions and surgical planning. Radiologists usually separate and analyze brain tumors by combining images of axial, coronal, and sagittal views. However, traditional convolutional neural network (CNN) models tend to use information from only a single view or one by one. Moreover, the existing models adopt a multi-branch structure with different-size convolution kernels in parallel to adapt to various tumor sizes. However, the difference in the convolution kernels’ parameters cannot precisely characterize the feature similarity of tumor lesion regions with various sizes, connectivity, and convexity. To address the above problems, we propose a hierarchical multi-view convolution method that decouples the standard 3D convolution into axial, coronal, and sagittal views to provide complementary-view features. Then, every pixel is classified by ensembling the discriminant results from the three views. Moreover, we propose a multi-branch kernel-sharing mechanism with a dilated rate to obtain parameter-consistent convolution kernels with different receptive fields. We use the BraTS2018 and BraTS2020 datasets for comparison experiments. The average Dice coefficients of the proposed network on the BraTS2020 dataset can reach 78.16%, 89.52%, and 83.05% for the enhancing tumor (ET), whole tumor (WT), and tumor core (TC), respectively, while the number of parameters is only 0.5 M. Compared with the baseline network for brain tumor segmentation, the accuracy was improved by 1.74%, 0.5%, and 2.19%, respectively.

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“…Besides, the various outcomes in the same datasets caused by different ML methodologies are a major concern for the application of ML in clinical. For instance, the results generated by a two-stage cascaded U-Net ( 73 ) and an RDAU-Net ( 74 ) using the BraTS 2019 training dataset which comprises 259 cases of HGG and 76 cases of LGG are various.…”

Section: Applications Of Ai-based On Medical Imaging In Gliomamentioning

confidence: 99%

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

Meng

Qiu

et al. 2022

Front. Oncol.

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Glioma is one of the most fatal primary brain tumors, and it is well-known for its difficulty in diagnosis and management. Medical imaging techniques such as magnetic resonance imaging (MRI), positron emission tomography (PET), and spectral imaging can efficiently aid physicians in diagnosing, treating, and evaluating patients with gliomas. With the increasing clinical records and digital images, the application of artificial intelligence (AI) based on medical imaging has reduced the burden on physicians treating gliomas even further. This review will classify AI technologies and procedures used in medical imaging analysis. Additionally, we will discuss the applications of AI in glioma, including tumor segmentation and classification, prediction of genetic markers, and prediction of treatment response and prognosis, using MRI, PET, and spectral imaging. Despite the benefits of AI in clinical applications, several issues such as data management, incomprehension, safety, clinical efficacy evaluation, and ethical or legal considerations, remain to be solved. In the future, doctors and researchers should collaborate to solve these issues, with a particular emphasis on interdisciplinary teamwork.

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RDAU-Net: Based on a Residual Convolutional Neural Network With DFP and CBAM for Brain Tumor Segmentation

Cited by 14 publications

References 44 publications

A Medical Image Segmentation Method Based on Improved UNet 3+ Network

A Medical Image Segmentation Method Based on Improved UNet 3+ Network

Brain Tumor Segmentation Network with Multi-View Ensemble Discrimination and Kernel-Sharing Dilated Convolution

Applications of Artificial Intelligence Based on Medical Imaging in Glioma: Current State and Future Challenges

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