ArticleDiagnostics (Basel, Switzerland)2023
Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed, 45 citations in OpenAlex.
- A two-stage foundation model for bladder tumor segmentation: An international multi-site studyEuropean journal of radiology artificial intelligence · 2026Article
- SDA-SwinNet: Swin-UNet with Dense Skip and Shift-ASPP for Retinal Vessel Segmentation.Sensors (Basel, Switzerland) · 2026Article
- 3D brain tumor segmentation using an improved V-Net architecture and 3D attention gate.Neuroimage. Reports · 2026Article
- Systematic Evaluation of Atrous Spatial Pyramid Pooling in U-Net for Pore Segmentation in Plasma Electrolytic Oxidation Coatings.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- CDCG-UNet: Chaotic Optimization Assisted Brain Tumor Segmentation Based on Dilated Channel Gate Attention U-Net Model.Neuroinformatics · 2025Article
- XAI-MRI: an ensemble dual-modality approach for 3D brain tumor segmentation using magnetic resonance imaging.Frontiers in artificial intelligence · 2025Article
- Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework.Scientific reports · 2024Article
- G-Net: Implementing an enhanced brain tumor segmentation framework using semantic segmentation design.PloS one · 2024Article
- A Heart Image Segmentation Method Based on Position Attention Mechanism and Inverted Pyramid.Sensors (Basel, Switzerland) · 2023Article
- Segmentation of Brain Tumor Using a 3D Generative Adversarial Network.Diagnostics (Basel, Switzerland) · 2023Article
- Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation.Journal of medical physicsReview
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
Abstract
Brain tumor segmentation from Magnetic Resonance Images (MRI) is considered a big challenge due to the complexity of brain tumor tissues, and segmenting these tissues from the healthy tissues is an even more tedious challenge when manual segmentation is undertaken by radiologists. In this paper, we have presented an experimental approach to emphasize the impact and effectiveness of deep learning elements like optimizers and loss functions towards a deep learning optimal solution for brain tumor segmentation. We evaluated our performance results on the most popular brain tumor datasets (MICCAI BraTS 2020 and RSNA-ASNR-MICCAI BraTS 2021). Furthermore, a new Bridged U-Net-ASPP-EVO was introduced that exploits Atrous Spatial Pyramid Pooling to enhance capturing multi-scale information to help in segmenting different tumor sizes, Evolving Normalization layers, squeeze and excitation residual blocks, and the max-average pooling for down sampling. Two variants of this architecture were constructed (Bridged U-Net_ASPP_EVO v1 and Bridged U-Net_ASPP_EVO v2). The best results were achieved using these two models when compared with other state-of-the-art models; we have achieved average segmentation dice scores of 0.84, 0.85, and 0.91 from variant1, and 0.83, 0.86, and 0.92 from v2 for the Enhanced Tumor (ET), Tumor Core (TC), and Whole Tumor (WT) tumor sub-regions, respectively, in the BraTS 2021validation dataset.
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