Segmentation of brain tumor MRI image based on improved attention module Unet network

Zhang, Lei; Lan, Chaofeng; Fu, Lirong; Mao, Xiuhuan; Zhang, Meng

doi:10.1007/s11760-022-02443-5

Cited by 8 publications

(7 citation statements)

References 21 publications

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“…Both [88] and [93] provide fused and segmented images. An improved U-Net architecture called InR-ResCBAM-U-net [117] was used for simplified training of DNN to achieve segmentation with higher accuracy. A CNN with U-Net based model was proposed in [118] to rectify the problem of tumor segmentation and the brain tumor was classified as non-enhancing tumor, necrosis, enhancing tumor and edema.…”

Section: B U-net Based Segmentation Of Brain Tumormentioning

confidence: 99%

“…In this FLAIR MRI scan was used for the segmentation of WT, TC and ET. The difference between the variants of U-Net architectures proposed in [69], [88], [93], [98], [99], [100], [101], [103], [106], [108], [109], [110], [111], [112], [116] and [117] are summarized in Table V. The U-Net architecture was modified by hybridizing the network [120] with residual block and attention block (in between the concatenation of down sampling part with up sampling part) and deep supervision block at the end of the decoder part from multi-resolution T1c, T2 and FLAIR MRI images.…”

Section: B U-net Based Segmentation Of Brain Tumormentioning

confidence: 99%

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Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Preetha,

Priyadarsini,

Nisha

2023

IEEE Access

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The state-of-the-art works for the segmentation of brain tumor using the images acquired by Magnetic Resonance Imaging (MRI) with their performances are analyzed in this comparative study. First, the architectures of convolutional neural networks (CNN) and the variants of U-shaped Network (U-Net), a kind of Deep Neural Network (DNN) are compared and their differences are highlighted. The publicly available datasets of MRI images specifically Brain Tumor Segmentation (BraTS) are also discussed. Next, the performances of tumor segmentation of various methods in the literature are compared using the parameters such as Dice score and Hausdroff distance ( 95). This study concludes that the U-Net based architectures using the BraTS-2019 dataset outperform well compared with other CNN based architectures.

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Section: B U-net Based Segmentation Of Brain Tumormentioning

confidence: 99%

Section: B U-net Based Segmentation Of Brain Tumormentioning

confidence: 99%

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Preetha,

Priyadarsini,

Nisha

2023

IEEE Access

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“…proposed a study on medical image segmentation through improving the U-Net model, which has strong robustness and practicality in medical image detection. Wu [4] , etc. proposed a brain tumor image segmentation algorithm based on Unet's attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

System for detecting adhesive overflow in sensor chips based on improved Unet

chen,

Long,

Tang

et al. 2024

International Conference on Optoelectronic Materials and Devices (ICOMD 2023)

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“…For instance, Zhang et al [20] incorporated residual connections into U-Net, allowing the network to extract deep-level features from MRI data. Zhang et al [21] introduced attention mechanisms into U-Net, enabling the network to focus on relevant features and thereby improving the segmentation accuracy of BG. Takahashi et al [22] employed a two-step training strategy for network optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al. [21] introduced attention mechanisms into U‐Net, enabling the network to focus on relevant features and thereby improving the segmentation accuracy of BG. Takahashi et al.…”

Section: Introductionmentioning

confidence: 99%

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Yin

Gao

Gong

et al. 2023

IET Image Processing

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Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.

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Segmentation of brain tumor MRI image based on improved attention module Unet network

Cited by 8 publications

References 21 publications

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

System for detecting adhesive overflow in sensor chips based on improved Unet

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

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