ArticleSensors (Basel, Switzerland)2022
Attention 3D U-Net with Multiple Skip Connections for Segmentation of Brain Tumor Images.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- Deep Learning for Automated Detection and Segmentation of Meningioma on Multiparametric MRI.Journal of imaging informatics in medicine · 2026Article
- A deep learning framework for automated dental segmentation and diagnostic report generation from cone-beam computed tomography.Head & face medicine · 2025Article
- Hybrid transfer learning and self-attention framework for robust MRI-based brain tumor classification.Scientific reports · 2025Article
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- Evaluating Deep Learning Architectures for Actin Filament Segmentation under Varying Noise Conditions in Simulated Cryo-ET Tomograms.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2025Article
- A Bayesian deep segmentation framework for glioblastoma tumor segmentation using follow-up MRIs.Frontiers in neuroimaging · 2025Article
- Automatic diagnosis and measurement of intracranial aneurysms using deep learning in MRA raw images.Frontiers in neurology · 2025Article
- CNN-Based Kidney Segmentation Using a Modified CLAHE Algorithm.Sensors (Basel, Switzerland) · 2024Article
- Automated volumetric analysis of the inner ear fluid space from hydrops magnetic resonance imaging using 3D neural networks.Scientific reports · 2024Article
- Automated segmentation and source prediction of bone tumors using ConvNeXtv2 Fusion based Mask R-CNN to identify lung cancer metastasis.Journal of bone oncology · 2024Article
- Article
- Enhancing brain tumor segmentation in MRI images using the IC-net algorithm framework.Scientific reports · 2024Article
- Enhancing Automated Brain Tumor Detection Accuracy Using Artificial Intelligence Approaches for Healthcare Environments.Bioengineering (Basel, Switzerland) · 2024Article
- MARes-Net: multi-scale attention residual network for jaw cyst image segmentation.Frontiers in bioengineering and biotechnology · 2024Article
- Deep learning for MRI lesion segmentation in rectal cancer.Frontiers in medicine · 2024Review
- Enhancing COVID-19 CT Image Segmentation: A Comparative Study of Attention and Recurrence in UNet Models.Journal of imaging · 2023Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- New Approach for Generating Synthetic Medical Data to Predict Type 2 Diabetes.Bioengineering (Basel, Switzerland) · 2023Article
- Brain Tumor Detection Based on Deep Learning Approaches and Magnetic Resonance Imaging.Cancers · 2023Article
- TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet Transform Domain.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
Among researchers using traditional and new machine learning and deep learning techniques, 2D medical image segmentation models are popular. Additionally, 3D volumetric data recently became more accessible, as a result of the high number of studies conducted in recent years regarding the creation of 3D volumes. Using these 3D data, researchers have begun conducting research on creating 3D segmentation models, such as brain tumor segmentation and classification. Since a higher number of crucial features can be extracted using 3D data than 2D data, 3D brain tumor detection models have increased in popularity among researchers. Until now, various significant research works have focused on the 3D version of the U-Net and other popular models, such as 3D U-Net and V-Net, while doing superior research works. In this study, we used 3D brain image data and created a new architecture based on a 3D U-Net model that uses multiple skip connections with cost-efficient pretrained 3D MobileNetV2 blocks and attention modules. These pretrained MobileNetV2 blocks assist our architecture by providing smaller parameters to maintain operable model size in terms of our computational capability and help the model to converge faster. We added additional skip connections between the encoder and decoder blocks to ease the exchange of extracted features between the two blocks, which resulted in the maximum use of the features. We also used attention modules to filter out irrelevant features coming through the skip connections and, thus, preserved more computational power while achieving improved accuracy.
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