Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity

Liu, Xiangbin; Hou, Shufen; Liu, Shuai; Ding, Weiping; Zhang, Yudong

doi:10.1016/j.jksuci.2023.03.011

Cited by 9 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The existing AMMGS 58 method had lower performance compared to other methods. Previous ResUNet+ 59 method and RBAF method had varying performances, with the latter showing relatively lower DSC values and higher Hausdorff 95 distances.…”

Section: Experimental Outcomesmentioning

confidence: 87%

“…The existing AMMGS 58 observed that the introduced method achieves higher performance than the conventional approaches.…”

Section: Achieved Performance Measures For Brats2020mentioning

confidence: 92%

“…This section discusses, the analysis of outcomes accomplished from various simulation experiment for BT segmentation and OSP. The existing methods such as ResUNet+, 53 radiomics based automatic framework (RBAF), 35 DCNN, 54 encoder–decoder method with depthwise atrous spatial pyramid pooling Network (EDD‐Net), 55 Weight loss function and Dropout U‐Net with convNeXt block (WD‐UNeXt), 56 focal cross transformer, 57 Attention‐based Multimodal Glioma Segmentation (AMMGS), 58 Segmentation based on Transformer and U2‐Net (STrans‐U2Net), 59 deep Residual U‐Net (dRes U‐Net), 60 and DenseTransformer (DenseTrans) 61 are compared with the introduced approach based on DSC, Sy, Sp and Hausdorff 95 in Table 6.…”

Section: Experimental Outcomesmentioning

confidence: 99%

See 2 more Smart Citations

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Ramkumar,

Kumar,

Padmapriya

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Brain tumor segmentation (BTS) from magnetic resonance imaging (MRI) scans is crucial for the diagnosis, treatment planning, and monitoring of therapeutic results. Thus, this research work proposes a novel graph momentum fully convolutional network with a modified Elman spike neural network (MESNN) for BTS and overall survival prediction (OSP). Initially, the introduced graph momentum fully convolutional network segments the brain tumor as enhanced tumor, the tumor core, and the whole tumor from the pre‐processed MRI scans. Second, the texture, intensity, shape, and wavelet features were extracted from the segmented tumors. Then, the horse herd optimization algorithm is utilized to minimize the feature's dimensionality. Finally, the OSP is performed by the MESNN which classifies the survival prediction of a patient as long‐term, mid‐term, and short‐term. The achieved segmentation accuracy of proposed method is 97% and the survival prediction's average RMSE is 215.5.

show abstract

Section: Experimental Outcomesmentioning

confidence: 87%

“…The existing AMMGS 58 observed that the introduced method achieves higher performance than the conventional approaches.…”

Section: Achieved Performance Measures For Brats2020mentioning

confidence: 92%

Section: Experimental Outcomesmentioning

confidence: 99%

See 1 more Smart Citation

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Ramkumar,

Kumar,

Padmapriya

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

show abstract

“…The main aim is to estimate mean values for such skewed histograms. The minimum case of the Gumbel distribution, which is derived from (6), is shown in (7) and (8); this is to estimate the mean values for the object and background samples in a given image.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…For instance, in some medical images, the skewness of a histogram can be influenced by factors such as the image acquisition parameters. When Magnetic Resonance Imaging (MRI) a brain, the segmentation task is affected if the intensity dissimilarity between adjacent glioma regions is small; this challenge was solved using U-Net to propose attention layers for small-intensity dissimilarities [7]. Moreover, Computed Tomography (CT) scan images can indeed vary depending on multiple factors, including the stage and type of infection, while the proportion of infected pixels in CT images is small [8].…”

Section: Introductionmentioning

confidence: 99%

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Jumiawi,

El-Zaart

2023

ASI

View full text Add to dashboard Cite

In this study, we delve into the realm of image segmentation, a field characterized by a multitude of approaches; one frequently used technique is thresholding-based image segmentation. This process divides intensity levels into different regions based on a specified threshold value. Minimum Cross-Entropy Thresholding (MCET) stands out as an independent objective function that can be applied with any distribution and is regarded as a mean-based thresholding method. In certain cases, images exhibit diverse structures that result in different histogram distributions. Some images possess symmetric histograms, while others feature asymmetric ones. Traditional mean-based thresholding methods are well-suited for symmetric image histograms, relying on Gaussian distribution definitions for mean estimations. However, in situations involving asymmetric distributions, such as left and right-skewed histograms, a different approach is required. In this paper, we propose the utilization of a Maximum Likelihood Estimation (MLE) of Gumbel’s distribution or Extreme Value Type I (EVI) distribution for the objective function of an MCET. Our goal is to introduce a dedicated image-thresholding model designed to enhance the accuracy and efficiency of image-segmentation tasks. This model determines optimal thresholds for image segmentation, facilitating precise data analysis for specific image types and yielding improved segmentation results by considering the impact of mean values on thresholding objective functions. We compare our proposed model with original methods and related studies in the literature. Our model demonstrates better performance in terms of segmentation accuracy, as assessed through both unsupervised and supervised evaluations for image segmentation.

show abstract

Relative likelihood based aggregated dual deep neural network for skin lesion recognition in dermoscopy images

Anand,

Sheeba,

Maha Tharshini

2024

Multimed Tools Appl

View full text Add to dashboard Cite

Attention-based multimodal glioma segmentation with multi-attention layers for small-intensity dissimilarity

Cited by 9 publications

References 34 publications

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Brain tumor segmentation and survival time prediction using graph momentum fully convolutional network with modified Elman spike neural network

Gumbel (EVI)-Based Minimum Cross-Entropy Thresholding for the Segmentation of Images with Skewed Histograms

Relative likelihood based aggregated dual deep neural network for skin lesion recognition in dermoscopy images

Contact Info

Product

Resources

About