Brain Tumour Segmentation Using Probabilistic U-Net

Savadikar, Chinmay; Kulhalli, Rahul; Garware, Bhushan

doi:10.1007/978-3-030-72087-2_22

Cited by 9 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The combined use of several atrous convolutions with different dilatation factors and attention mechanisms in one block makes the proposed architecture outperform the comparison architecture that uses the attention mechanism in the skip connection section in [39] as shown in Table 11. The proposed architecture also outperforms the architecture in [41], which uses probabilistic UNet by applying an attention mechanism to the skip connection. The performance of the architecture in [40] which adds deep supervision in the decoder section, and the architecture in [33] that uses the SEB block also has a lower average dice performance than the proposed architecture.…”

Section: Results Analysismentioning

confidence: 98%

“…In addition to implementing an attention gate in the skip connection section, Xu et al [40] also adds supervision in the segmentation layer in the expanding layer. Savadikar et al [41] used a 2D probabilistic UNet and the application of an attention mechanism on the skip connection section. Guo et al [42] applied a combination of two patch sizes and two types of attention (spatial attention and attention channel) and mixed them on UNet 3D to form four independent models for segmenting tumors.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

SDA-UNET2.5D: Shallow Dilated with Attention Unet2.5D for Brain Tumor Segmentation

2022

IJIES

View full text Add to dashboard Cite

Many studies have been carried out to segmentation brain tumors on 3D Magnetic Resonance Imaging (MRI) images with 3D or 2D approaches. The 3D approach pays attention to the interrelationships between slices in a 3D image. However, this requires high resources, while the 2D approach requires lower resources but ignores the voxel relationship in 3D space. The 2.5D approach seeks to combine the lightness of the 2D approach and the voxel interconnection of the 3D approach. This article proposes SDA-UNet2.5D, a shallow UNet 2.5D architecture that pays attention to the interconnectedness of 3D images by involving five slices to get one slice of segmentation prediction results. The architecture is trained using the Brain Tumor Segmentation (BraTS) 2018, 2019, and 2020 datasets. Compared to other architectures, this proposed architecture has a high segmentation speed with 4.05-4.24 seconds to segment one patient data. Online validation resulted in superior average dice

show abstract

Section: Results Analysismentioning

confidence: 98%

Section: Related Workmentioning

confidence: 99%

SDA-UNET2.5D: Shallow Dilated with Attention Unet2.5D for Brain Tumor Segmentation

2022

IJIES

View full text Add to dashboard Cite

show abstract

“…Savadikar C. et al. used probabilistic U-Net to explore the effect of sampling different segmentation maps, and at the same time explored the effect of changes in the number of attention modules on segmentation quality ( 37 ).…”

Section: Resultsmentioning

confidence: 99%

Overall Survival Prediction for Gliomas Using a Novel Compound Approach

et al. 2021

View full text Add to dashboard Cite

As a highly malignant tumor, the incidence and mortality of glioma are not optimistic. Predicting the survival time of patients with glioma by extracting the feature information from gliomas is beneficial for doctors to develop more targeted treatments. Magnetic resonance imaging (MRI) is a way to quickly and clearly capture the details of brain tissue. However, manually segmenting brain tumors from MRI will cost doctors a lot of energy, and doctors can only vaguely estimate the survival time of glioma patients, which are not conducive to the formulation of treatment plans. Therefore, automatically segmenting brain tumors and accurately predicting survival time has important significance. In this article, we first propose the NLSE-VNet model, which integrates the Non-Local module and the Squeeze-and-Excitation module into V-Net to segment three brain tumor sub-regions in multimodal MRI. Then extract the intensity, texture, wavelet, shape and other radiological features from the tumor area, and use the CNN network to extract the deep features. The factor analysis method is used to reduce the dimensionality of features, and finally the dimensionality-reduced features and clinical features such as age and tumor grade are combined into the random forest regression model to predict survival. We evaluate the effect on the BraTS 2019 and BraTS 2020 datasets. The average Dice of brain tumor segmentation tasks up to 79% and the average RMSE of the survival predictive task is as low as 311.5. The results indicate that the method in this paper has great advantages in segmentation and survival prediction of gliomas.

show abstract

“…To validate the efficiency of our method for brain tumor segmentation, we conducted extensive comparisons with recent 2D and 3D segmentation methodologies. For 2Dbased models, our comparative analysis included MTAU [9], Probabilistic U-Net [34], and AGResU-Net [11], all of which employ attention mechanisms to enhance the segmentation accuracy. For 3D-based models, we compared our method with several advanced architectures.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

TACA-RNet: Tri-Axis Based Context-Aware Reverse Network for Multimodal Brain Tumor Segmentation

Kim,

Jo,

Lee

et al. 2024

Electronics

View full text Add to dashboard Cite

Brain tumor segmentation using Magnetic Resonance Imaging (MRI) is vital for clinical decision making. Traditional deep learning-based studies using convolutional neural networks have predominantly processed MRI data as two-dimensional slices, leading to the loss of contextual information. While three-dimensional (3D) convolutional layers represent an advancement, they have not fully exploited pathological information according to the three-axis nature of 3D MRI data—axial, coronal, and sagittal. Recognizing these limitations, we introduce a Tri-Axis based Context-Aware Reverse Network (TACA-RNet). This innovative approach leverages the unique 3D spatial orientations of MRI, learning crucial information on brain anatomy and pathology. We incorporated three specialized modules: a Tri-Axis Channel Reduction module for optimizing feature dimensions, a MultiScale Contextual Fusion module for aggregating multi-scale features and enhancing spatial discernment, and a 3D Axis Reverse Attention module for the precise delineation of tumor boundaries. The TACA-RNet leverages three specialized modules to enhance the understanding of tumor characteristics and spatial relationships within MRI data by fully utilizing its tri-axial structure. Validated on the Brain Tumor Segmentation Challenge 2018 and 2020 datasets, the TACA-RNet demonstrated superior performances over contemporary methodologies. This underscores the critical role of leveraging the three-axis structure of MRI to enhance segmentation accuracy.

show abstract

Brain Tumour Segmentation Using Probabilistic U-Net

Cited by 9 publications

References 9 publications

SDA-UNET2.5D: Shallow Dilated with Attention Unet2.5D for Brain Tumor Segmentation

SDA-UNET2.5D: Shallow Dilated with Attention Unet2.5D for Brain Tumor Segmentation

Overall Survival Prediction for Gliomas Using a Novel Compound Approach

TACA-RNet: Tri-Axis Based Context-Aware Reverse Network for Multimodal Brain Tumor Segmentation

Contact Info

Product

Resources

About