ArticleDiagnostics (Basel, Switzerland)2023
An Intelligent Auxiliary Framework for Bone Malignant Tumor Lesion Segmentation in Medical Image Analysis.
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 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline 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
- Dynamic wavelet transform normalized multi scale dense attention UNet with Binary Horse Herd Optimization for osteosarcoma diagnosis.Scientific reports · 2026Article
- SAM3-AgeSeg: an adaptive segmentation model for bone tumors in the aging population.Frontiers in medicine · 2026Article
- Knowledge, attitudes, and practices toward nutritional management among patients with gastrointestinal cancer: A cross-sectional study.Asia-Pacific journal of oncology nursing · 2025Article
- 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
- Revolutionising osseous biopsy: the impact of artificial intelligence in the era of personalized medicine.The British journal of radiology · 2025Review
- Metastasis lesion segmentation from bone scintigrams using encoder-decoder architecture model with multi-attention and multi-scale learning.Quantitative imaging in medicine and surgery · 2025Article
- Data-Efficient Bone Segmentation Using Feature Pyramid- Based SegFormer.Sensors (Basel, Switzerland) · 2024Article
- FASNet: Feature alignment-based method with digital pathology images in assisted diagnosis medical system.Heliyon · 2024Article
- Intelligent cell images segmentation system: based on SDN and moving transformer.Scientific reports · 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
- Deep learning image segmentation approaches for malignant bone lesions: a systematic review and meta-analysis.Frontiers in radiology · 2023Review
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Authors and funding
7 authors.
Funding
Abstract
Bone malignant tumors are metastatic and aggressive, with poor treatment outcomes and prognosis. Rapid and accurate diagnosis is crucial for limb salvage and increasing the survival rate. There is a lack of research on deep learning to segment bone malignant tumor lesions in medical images with complex backgrounds and blurred boundaries. Therefore, we propose a new intelligent auxiliary framework for the medical image segmentation of bone malignant tumor lesions, which consists of a supervised edge-attention guidance segmentation network (SEAGNET). We design a boundary key points selection module to supervise the learning of edge attention in the model to retain fine-grained edge feature information. We precisely locate malignant tumors by instance segmentation networks while extracting feature maps of tumor lesions in medical images. The rich contextual-dependent information in the feature map is captured by mixed attention to better understand the uncertainty and ambiguity of the boundary, and edge attention learning is used to guide the segmentation network to focus on the fuzzy boundary of the tumor region. We implement extensive experiments on real-world medical data to validate our model. It validates the superiority of our method over the latest segmentation methods, achieving the best performance in terms of the Dice similarity coefficient (0.967), precision (0.968), and accuracy (0.996). The results prove the important contribution of the framework in assisting doctors to improve the accuracy of diagnosis and clinical efficiency.
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