3D PSwinBTS: An efficient transformer-based Unet using 3D parallel shifted windows for brain tumor segmentation

Liang, Junjie; Yang, Cihui; Zeng, Lingqiu

doi:10.1016/j.dsp.2022.103784

Cited by 19 publications

(9 citation statements)

References 16 publications

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“…3D PSwinBTS [126] 20.40 68.60 TRSF-Net [147] -274.09 SF-SegFormer [153] 1.00 -UNesT [155] 87.30 261.70 TW-Net [156] 30.09 5.81…”

Section: Methods Param (M) Flops (G)mentioning

confidence: 99%

“…[19] Hatamizadeh et al [125] proposed a Swin UNEt TRansformer (Swin UNETR) model replacing the encoder with the Swin Transformer [49] to capture multi-scale features in the partitioning scheme with shifted windows. Similar to the Swin UNETR, [125] Liang et al [126] embedded the Swin Transformer [49] into the encoder structure using parallel shift windows. They further supplemented prior knowledge to realize semantic modeling more efficiently.…”

Section: Brain Tumor Segmentationmentioning

confidence: 99%

“…Finally, the feature fusion module combined different features to supervise the training for the Transformer. Nian et al [142] . developed a plug‐and‐play Infinite Deformable Fusion Transformer Module (IDFTM) that combined different attention heads using a fusion self‐attention mechanism with attentional logic and weight mapping.…”

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

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Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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“…3D PSwinBTS [126] 20.40 68.60 TRSF-Net [147] -274.09 SF-SegFormer [153] 1.00 -UNesT [155] 87.30 261.70 TW-Net [156] 30.09 5.81…”

Section: Methods Param (M) Flops (G)mentioning

confidence: 99%

Section: Brain Tumor Segmentationmentioning

confidence: 99%

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

See 1 more Smart Citation

Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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show abstract

“…Similarly, the scholarly paper authored by Nian et al [32] presents a novel brain tumor segmentation strategy via a 3D transformer-based approach. The Fusion-Head Self-Attention Mechanism (FHSA) leverages the long-range spatial dependency of 3D MRI data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The residual mix transformer fusion net is employed in reference [28], where it results in a dice score of 0.821. Also using the Infinite Deformable Fusion Transformer Module (IDFTM) with PSwinBTS references [32] and [35]achieve dice scores of 89.2% and 88.65%, respectively.…”

Section: Comparative Analysismentioning

confidence: 99%

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Zakariah,

Al-Razgan,

Alfakih

2024

Preprint

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Brain tumors are one of the leading causes of cancer death; screening early is the best strategy to diagnose and treat brain tumors. Magnetic Resonance Imaging (MRI) is extensively utilized for brain tumor diagnosis; nevertheless, achieving improved accuracy and performance, which is a critical challenge in most of the previously reported automated medical diagnostics, is a difficult problem. The study introduces the Dual Vision Transformer-DSUNET model, which incorporates feature fusion techniques to provide precise and efficient differentiation between brain tumors and other brain regions by leveraging multi-modal MRI data. The impetus for this study arises from the necessity of automating the segmentation process of brain tumors in medical imaging, a critical component in the realms of diagnosis and therapy strategy. To tackle this issue the BRATS 2020 dataset is employed, an extensively utilized dataset for the segmentation of brain tumors. This dataset encompasses multi-modal MRI images, including T1-weighted, T2-weighted, T1Gd (contrast-enhanced), and FLAIR modalities. The proposed model incorporates the dual vision idea to comprehensively capture the heterogeneous properties of brain tumors across several imaging modalities. Moreover, the utilization of feature fusion techniques is implemented to augment the amalgamation of data originating from several modalities, hence enhancing the accuracy and dependability of tumor segmentation. The evaluation of the Dual Vision Transformer-DSUNET model's performance is conducted by employing the Dice Coefficient as a prevalent metric for quantifying segmentation accuracy. The results obtained from the experiment exhibit remarkable performance, with Dice Coefficient values of 91.47% for enhanced tumors, 92.38% for core tumors, and 90.88% for edema. The cumulative Dice score for the entirety of the classes is 91.29%. In addition, the model has a high level of accuracy, roughly 99.93%, which underscores its durability and efficacy in the task of segmenting brain tumors. Experimental findings demonstrate the integrity of the suggested architecture, which has quickly improved the detection accuracy of many brain diseases.

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Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion

Pan,

Yong,

et al. 2024

J Applied Clin Med Phys

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Background and objectiveAccurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) holds significant importance in clinical diagnosis and surgical intervention, while current deep learning methods cope with situations of multimodal MRI by an early fusion strategy that implicitly assumes that the modal relationships are linear, which tends to ignore the complementary information between modalities, negatively impacting the model's performance. Meanwhile, long‐range relationships between voxels cannot be captured due to the localized character of the convolution procedure.MethodAiming at this problem, we propose a multimodal segmentation network based on a late fusion strategy that employs multiple encoders and a decoder for the segmentation of brain tumors. Each encoder is specialized for processing distinct modalities. Notably, our framework includes a feature fusion module based on a 3D discrete wavelet transform aimed at extracting complementary features among the encoders. Additionally, a 3D global context‐aware module was introduced to capture the long‐range dependencies of tumor voxels at a high level of features. The decoder combines fused and global features to enhance the network's segmentation performance.ResultOur proposed model is experimented on the publicly available BraTS2018 and BraTS2021 datasets. The experimental results show competitiveness with state‐of‐the‐art methods.ConclusionThe results demonstrate that our approach applies a novel concept for multimodal fusion within deep neural networks and delivers more accurate and promising brain tumor segmentation, with the potential to assist physicians in diagnosis.

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3D PSwinBTS: An efficient transformer-based Unet using 3D parallel shifted windows for brain tumor segmentation

Cited by 19 publications

References 16 publications

Understanding the brain with attention: A survey of transformers in brain sciences

Understanding the brain with attention: A survey of transformers in brain sciences

Dual Vision Transformer-DSUNET With Feature Fusion for Brain Tumor Segmentation

Brain tumor segmentation by combining MultiEncoder UNet with wavelet fusion

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