ArticleComputational intelligence and neuroscience2022
A Residual Fusion Network for Osteosarcoma MRI Image Segmentation in Developing Countries.
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 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning applications in osteosarcoma MRI: A systematic review of recent advances in AI-based osteosarcoma diagnosis.PloS one · 2026Pooled it
- Article
- FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system.Heliyon · 2024Article
- High-quality expert annotations enhance artificial intelligence model accuracy for osteosarcoma X-ray diagnosis.Cancer science · 2024Article
- 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
- 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
- Artificial intelligence applications in pediatric oncology diagnosis.Exploration of targeted anti-tumor therapy · 2023Review
- AI-Assisted Diagnosis and Decision-Making Method in Developing Countries for Osteosarcoma.Healthcare (Basel, Switzerland) · 2022Article
- A Multimodal Auxiliary Classification System for Osteosarcoma Histopathological Images Based on Deep Active Learning.Healthcare (Basel, Switzerland) · 2022Article
- Auxiliary Segmentation Method of Osteosarcoma in MRI Images Based on Denoising and Local Enhancement.Healthcare (Basel, Switzerland) · 2022Article
- Auxiliary Segmentation Method of Osteosarcoma MRI Image Based on Transformer and U-Net.Computational intelligence and neuroscience · 2022Article
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4 authors.
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Abstract
Among primary bone cancers, osteosarcoma is the most common, peaking between the ages of a child's rapid bone growth and adolescence. The diagnosis of osteosarcoma requires observing the radiological appearance of the infected bones. A common approach is MRI, but the manual diagnosis of MRI images is prone to observer bias and inaccuracy and is rather time consuming. The MRI images of osteosarcoma contain semantic messages in several different resolutions, which are often ignored by current segmentation techniques, leading to low generalizability and accuracy. In the meantime, the boundaries between osteosarcoma and bones or other tissues are sometimes too ambiguous to separate, making it a challenging job for inexperienced doctors to draw a line between them. In this paper, we propose using a multiscale residual fusion network to handle the MRI images. We placed a novel subnetwork after the encoders to exchange information between the feature maps of different resolutions, to fuse the information they contain. The outputs are then directed to both the decoders and a shape flow block, used for improving the spatial accuracy of the segmentation map. We tested over 80,000 osteosarcoma MRI images from the PET-CT center of a well-known hospital in China. Our approach can significantly improve the effectiveness of the semantic segmentation of osteosarcoma images. Our method has higher F1, DSC, and IOU compared with other models while maintaining the number of parameters and FLOPS.
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