ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2016
Atlas-based rib-bone detection in chest X-rays.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 36 citations in OpenAlex.
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- Gray-Scale Extraction of Bone Features from Chest Radiographs Based on Deep Learning Technique for Personal Identification and Classification in Forensic Medicine.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning for Chest X-ray Diagnosis: Competition Between Radiologists with or Without Artificial Intelligence Assistance.Journal of imaging informatics in medicine · 2024Article
- Can Deep Adult Lung Segmentation Models Generalize to the Pediatric Population?Expert systems with applications · 2023Article
- A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs.Bioengineering (Basel, Switzerland) · 2022Article
- Anatomic Point-Based Lung Region with Zone Identification for Radiologist Annotation and Machine Learning for Chest Radiographs.Journal of digital imaging · 2021Article
- Chest X-ray Bone Suppression for Improving Classification of Tuberculosis-Consistent Findings.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
This paper investigates using rib-bone atlases for automatic detection of rib-bones in chest X-rays (CXRs). We built a system that takes patient X-ray and model atlases as input and automatically computes the posterior rib borders with high accuracy and efficiency. In addition to conventional atlas, we propose two alternative atlases: (i) automatically computed rib bone models using Computed Tomography (CT) scans, and (ii) dual energy CXRs. We test the proposed approach with each model on 25 CXRs from the Japanese Society of Radiological Technology (JSRT) dataset and another 25 CXRs from the National Library of Medicine CXR dataset. We achieve an area under the ROC curve (AUC) of about 95% for Montgomery and 91% for JSRT datasets. Using the optimal operating point of the ROC curve, we achieve a segmentation accuracy of 88.91±1.8% for Montgomery and 85.48±3.3% for JSRT datasets. Our method produces comparable results with the state-of-the-art algorithms. The performance of our method is also excellent on challenging X-rays as it successfully addressed the rib-shape variance between patients and number of visible rib-bones due to patient respiration.
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Registered trials
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