ArticleComputational intelligence and neuroscience2022
Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled it
- Evaluating vision transformers and convolutional neural networks in the context of dental image processing: a systematic review.BMC oral health · 2025Pooled it
- Deep learning for automated segmentation of central cartilage tumors on MRI.European radiology experimental · 2025Article
- Artificial Intelligence in Primary Malignant Bone Tumor Imaging: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- Review
- Research on Artificial-Intelligence-Assisted Medicine: A Survey on Medical Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Review
- An Innovative Solution Based on TSCA-ViT for Osteosarcoma Diagnosis in Resource-Limited Settings.Biomedicines · 2023Article
- Comparative Analysis of Image Processing Techniques for Enhanced MRI Image Quality: 3D Reconstruction and Segmentation Using 3D U-Net Architecture.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence-Aided Diagnosis Solution by Enhancing the Edge Features of Medical Images.Diagnostics (Basel, Switzerland) · 2023Article
- An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.Diagnostics (Basel, Switzerland) · 2023Article
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
- AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma.Healthcare (Basel, Switzerland) · 2022Article
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Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most prevalent malignant bone tumors is osteosarcoma. The diagnosis and treatment cycle are long and the prognosis is poor. It takes a lot of time to manually identify osteosarcoma from osteosarcoma magnetic resonance imaging (MRI). Medical image processing technology has greatly alleviated the problems faced by medical diagnoses. However, MRI images of osteosarcoma are characterized by high noise and blurred edges. The complex features increase the difficulty of lesion area identification. Therefore, this study proposes an osteosarcoma MRI image segmentation method (OSTransnet) based on Transformer and U-net. This technique primarily addresses the issues of fuzzy tumor edge segmentation and overfitting brought on by data noise. First, we optimize the dataset by changing the precise spatial distribution of noise and the data-increment image rotation process. The tumor is then segmented based on the model of U-Net and Transformer with edge improvement. It compensates for the limitations of U-semantic Net by using channel-based transformers. Finally, we also add an edge enhancement module (BAB) and a combined loss function to improve the performance of edge segmentation. The method's accuracy and stability are demonstrated by the detection and training results based on more than 4,000 MRI images of osteosarcoma, which also demonstrate how well the method works as an adjunct to clinical diagnosis and treatment.
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