ArticleLa Radiologia medica2020
Computational techniques to segment and classify lumbar compression fractures.
Article in La Radiologia medica, 2020. 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, 27 citations in OpenAlex.
- A deep-learning model for identifying fresh vertebral compression fractures on digital radiography.European radiology · 2022Trial
- Radiomics Based on Multimodal magnetic resonance imaging for the Differential Diagnosis of Benign and Malignant Vertebral Compression Fractures.Orthopaedic surgery · 2024Article
- A 3D Radiomics-Based Artificial Neural Network Model for Benign Versus Malignant Vertebral Compression Fracture Classification in MRI.Journal of digital imaging · 2023Article
- Opportunistic Screening Techniques for Analysis of CT Scans.Current osteoporosis reports · 2023Review
- Artificial Intelligence in Emergency Radiology: Where Are We Going?Diagnostics (Basel, Switzerland) · 2022Review
- Automated Vertebral Segmentation and Measurement of Vertebral Compression Ratio Based on Deep Learning in X-Ray Images.Journal of digital imaging · 2021Article
- T2-mapping MRI evaluation of patellofemoral cartilage in patients submitted to intra-articular platelet-rich plasma (PRP) injections.La Radiologia medica · 2021Article
- A deep learning algorithm for automated measurement of vertebral body compression from X-ray images.Scientific reports · 2021Article
- Imaging side effects and complications of chemotherapy and radiation therapy: a pictorial review from head to toe.Insights into imaging · 2021Review
- Prediction of Incidental Osteoporotic Fractures at Vertebral-Specific Level Using 3D Non-Linear Finite Element Parameters Derived from Routine Abdominal MDCT.Diagnostics (Basel, Switzerland) · 2021Article
- Long-term therapeutic effect of percutaneous kyphoplasty combined with & without back muscle rehabilitation exercise in elderly patients. A comparative study.Pakistan journal of medical sciencesArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Vertebral fractures are important indicators of osteoporosis. Fractures with partial collapse of vertebral bodies are referred to as vertebral compression fractures (VCFs) that are usually non-traumatic in nature. Some common causes of VCFs are trauma, bone failure related to osteoporosis (benign) and metastatic cancer (malignant). This paper aims at developing a system for computer-aided diagnosis to help in the detection, labeling and segmentation of lumbar vertebral body (VB) and to further classify each VB into normal, malignant and benign VCFs. After the initial preprocessing, morphological, shape and angular features are used in the detection, labeling and segmentation steps. Various shape and statistical texture features are extracted from the segmented VB and are fed to the classifier for the final decision. The segmentation and classification results obtained were compared with the ground truth manual segmentation of the lumbar VB and the decision labels of the fractures provided by the experts. The dice similarity coefficient (DSC) for segmentation reached up to 94.27%, and the classification results show that shape and texture features together are able to correctly classify with an accuracy rate of 95.34%. The final outcomes are expected to be useful in the analysis of vertebral compression fractures.
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