ArticleComputational and mathematical methods in medicine2022
Intelligent Segmentation Medical Assistance System for MRI Images of Osteosarcoma in Developing Countries.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 2 of them syntheses that pooled it.
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Who cites it
25 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Performance comparison and future perspectives of deep learning and classical machine learning in bone tumor applications: a systematic review (2019-2025).BMC medical informatics and decision making · 2026Pooled it
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled 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
- Profile and clinical significance of SPARCL1 and its prognostic significance in breast cancer.Oncology letters · 2025Article
- Bone tumor recognition strategy based on object region and context representation in medical decision-making system.Scientific reports · 2025Article
- 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
- Article
- Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model.Cancers · 2022Article
- AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma.Healthcare (Basel, Switzerland) · 2022Article
- Deep Learning Algorithms in the Automatic Segmentation of Liver Lesions in Ultrasound Investigations.Life (Basel, Switzerland) · 2022Article
- A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning.Healthcare (Basel, Switzerland) · 2022Article
- An updated systematic review of radiomics in osteosarcoma: utilizing CLAIM to adapt the increasing trend of deep learning application in radiomics.Insights into imaging · 2022Article
- Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement.Healthcare (Basel, Switzerland) · 2022Article
- Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model.Healthcare (Basel, Switzerland) · 2022Article
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7 authors.
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Abstract
Osteosarcoma is the most common primary malignant bone tumor in children and adolescents. It has a high degree of malignancy and a poor prognosis in developing countries. The doctor manually explained that magnetic resonance imaging (MRI) suffers from subjectivity and fatigue limitations. In addition, the structure, shape, and position of osteosarcoma are complicated, and there is a lot of noise in MRI images. Directly inputting the original data set into the automatic segmentation system will bring noise and cause the model's segmentation accuracy to decrease. Therefore, this paper proposes an osteosarcoma MRI image segmentation system based on a deep convolution neural network, which solves the overfitting problem caused by noisy data and improves the generalization performance of the model. Firstly, we use Mean Teacher to optimize the data set. The noise data is put into the second round of training of the model to improve the robustness of the model. Then, we segment the image using a deep separable U-shaped network (SepUNet) and conditional random field (CRF). SepUnet can segment lesion regions of different sizes at multiple scales; CRF further optimizes the boundary. Finally, this article calculates the area of the tumor area, which provides a more intuitive reference for assisting doctors in diagnosis. More than 80000 MRI images of osteosarcoma from three hospitals in China were tested. The results show that the proposed method guarantees the balance of speed, accuracy, and cost under the premise of improving accuracy.
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