Automated estimation of total lung volume using chest radiographs and deep learning
| العنوان: | Automated estimation of total lung volume using chest radiographs and deep learning |
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| المؤلفون: | Sogancioglu, E., Murphy, K., Scholten, E.T., Boulogne, L.H., Prokop, M., Ginneken, B. van |
| المصدر: | Medical Physics, 49, 7, pp. 4466-4477 Medical Physics, 49, 4466-4477 |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Vision and Pattern Recognition (cs.CV), Image and Video Processing (eess.IV), Vascular damage Radboud Institute for Health Sciences [Radboudumc 16], Computer Science - Computer Vision and Pattern Recognition, General Medicine, respiratory system, Electrical Engineering and Systems Science - Image and Video Processing, Thorax, Machine Learning (cs.LG), respiratory tract diseases, Deep Learning, FOS: Electrical engineering, electronic engineering, information engineering, Humans, Radiography, Thoracic, Lung Volume Measurements, Lung, Rare cancers Radboud Institute for Health Sciences [Radboudumc 9], Retrospective Studies |
| الوصف: | Total lung volume is an important quantitative biomarker and is used for the assessment of restrictive lung diseases. In this study, we investigate the performance of several deep-learning approaches for automated measurement of total lung volume from chest radiographs. 7621 posteroanterior and lateral view chest radiographs (CXR) were collected from patients with chest CT available. Similarly, 928 CXR studies were chosen from patients with pulmonary function test (PFT) results. The reference total lung volume was calculated from lung segmentation on CT or PFT data, respectively. This dataset was used to train deep-learning architectures to predict total lung volume from chest radiographs. The experiments were constructed in a step-wise fashion with increasing complexity to demonstrate the effect of training with CT-derived labels only and the sources of error. The optimal models were tested on 291 CXR studies with reference lung volume obtained from PFT. The optimal deep-learning regression model showed an MAE of 408 ml and a MAPE of 8.1\% and Pearson's r = 0.92 using both frontal and lateral chest radiographs as input. CT-derived labels were useful for pre-training but the optimal performance was obtained by fine-tuning the network with PFT-derived labels. We demonstrate, for the first time, that state-of-the-art deep learning solutions can accurately measure total lung volume from plain chest radiographs. The proposed model can be used to obtain total lung volume from routinely acquired chest radiographs at no additional cost and could be a useful tool to identify trends over time in patients referred regularly for chest x-rays. Under review |
| وصف الملف: | application/pdf |
| تدمد: | 2473-4209 0094-2405 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d3e2547971ad98e98be7a6043c38e26c https://pubmed.ncbi.nlm.nih.gov/35388486 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....d3e2547971ad98e98be7a6043c38e26c |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 24734209 00942405 |
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