ReviewBMC bioinformatics2022
A state-of-the-art technique to perform cloud-based semantic segmentation using deep learning 3D U-Net architecture.
Review in BMC bioinformatics, 2022. 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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Who cites it
11 citing papers in PubMed.
- Balancing accuracy and efficiency in lumbar spine image segmentation using multi-scale attention and residual learning.Scientific reports · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Quantum-edge synergy for dynamic acoustic compensation in ultrasound safety: a narrative review of tissue heterogeneity challenges.Frontiers in medical technology · 2026Review
- Contouring in transition: perceptions of AI-based autocontouring by radiation oncologists and medical physicists in German-speaking countries.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Automated segmentation of the primary tumor in nasopharyngeal carcinoma using a deep learning framework in positron emission tomography imaging: a comparative study.Quantitative imaging in medicine and surgery · 2025Article
- Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).Quantitative imaging in medicine and surgery · 2024Article
- Understanding New Machine Learning Architectures: Practical Generative Artificial Intelligence for Anesthesiologists.Anesthesiology · 2024Review
- Deep learning for MRI lesion segmentation in rectal cancer.Frontiers in medicine · 2024Review
- Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges.Diagnostics (Basel, Switzerland) · 2023Review
- Deep learning-assisted radiomics facilitates multimodal prognostication for personalized treatment strategies in low-grade glioma.Scientific reports · 2023Article
- Overview of Deep Learning Algorithms and Optimizers for Brain Tumor Segmentation.Journal of medical physicsReview
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glioma is the most aggressive and dangerous primary brain tumor with a survival time of less than 14 months. Segmentation of tumors is a necessary task in the image processing of the gliomas and is important for its timely diagnosis and starting a treatment. Using 3D U-net architecture to perform semantic segmentation on brain tumor dataset is at the core of deep learning. In this paper, we present a unique cloud-based 3D U-Net method to perform brain tumor segmentation using BRATS dataset. The system was effectively trained by using Adam optimization solver by utilizing multiple hyper parameters. We got an average dice score of 95% which makes our method the first cloud-based method to achieve maximum accuracy. The dice score is calculated by using Sørensen-Dice similarity coefficient. We also performed an extensive literature review of the brain tumor segmentation methods implemented in the last five years to get a state-of-the-art picture of well-known methodologies with a higher dice score. In comparison to the already implemented architectures, our method ranks on top in terms of accuracy in using a cloud-based 3D U-Net framework for glioma segmentation.
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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.