ArticleDiagnostics (Basel, Switzerland)2023
U-Net-Based Models towards Optimal MR Brain Image 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 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Automated Ventricle Assessment via Three-dimensional Anatomical Reconstruction (AVA-TAR): a computational toolkit for autonomous lateral ventricle assessment in preclinical hydrocephalus models.bioRxiv : the preprint server for biology · 2026Article
- Development of a Preliminary Diagnostic Tool for the Segmentation of Benign Jaw Lesions in CBCT Images Using nnU-Net v2: An Artificial Intelligence-Based Approach.Journal of imaging informatics in medicine · 2026Article
- Automated transperineal ultrasound analysis using deep learning for pelvic floor dysfunction assessment after total hysterectomy.Frontiers in oncology · 2026Article
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- Transfer learning based osteoporosis prediction using enhanced medical imaging and fuzzy fusion.Scientific reports · 2025Article
- Utilizing Artificial Intelligence for CSF Segmentation and Analysis in Head CT Imaging: A Systematic Review.Brain sciences · 2025Review
- Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images.Scientific reports · 2025Article
- Gastrointestinal bleeding detection on digital subtraction angiography using convolutional neural networks with and without temporal information.Diagnostic and interventional radiology (Ankara, Turkey) · 2025Article
- AG-MS3D-CNN multiscale attention guided 3D convolutional neural network for robust brain tumor segmentation across MRI protocols.Scientific reports · 2025Article
- Deep learning-based segmentation of the trigeminal nerve and surrounding vasculature in trigeminal neuralgia.Journal of neurosurgery · 2025Article
- Research on Enhanced Dynamic Pig Counting Based on YOLOv8n and Deep SORT.Sensors (Basel, Switzerland) · 2025Article
- Automated liver and spleen segmentation for MR elastography maps using U-Nets.Scientific reports · 2025Article
- Transformers for Neuroimage Segmentation: Scoping Review.Journal of medical Internet research · 2025Article
- Flood change detection model based on an improved U-net network and multi-head attention mechanism.Scientific reports · 2025Article
- A deep learning-assisted automatic measurement of tear meniscus height on ocular surface images and its application in myopia control.Frontiers in bioengineering and biotechnology · 2025Article
- Deep learning-based automated segmentation and quantification of the dural sac cross-sectional area in lumbar spine MRI.Frontiers in radiology · 2025Article
- LIU-NET: lightweight Inception U-Net for efficient brain tumor segmentation from multimodal 3D MRI images.PeerJ. Computer science · 2025Article
- Deep learning for enhanced prediction of diabetic retinopathy: a comparative study on the diabetes complications data set.Frontiers in medicine · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Brain tumor segmentation from MRIs has always been a challenging task for radiologists, therefore, an automatic and generalized system to address this task is needed. Among all other deep learning techniques used in medical imaging, U-Net-based variants are the most used models found in the literature to segment medical images with respect to different modalities. Therefore, the goal of this paper is to examine the numerous advancements and innovations in the U-Net architecture, as well as recent trends, with the aim of highlighting the ongoing potential of U-Net being used to better the performance of brain tumor segmentation. Furthermore, we provide a quantitative comparison of different U-Net architectures to highlight the performance and the evolution of this network from an optimization perspective. In addition to that, we have experimented with four U-Net architectures (3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net) on the BraTS 2020 dataset for brain tumor segmentation to provide a better overview of this architecture's performance in terms of Dice score and Hausdorff distance 95%. Finally, we analyze the limitations and challenges of medical image analysis to provide a critical discussion about the importance of developing new architectures in terms of optimization.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.