Detecting the pulmonary trunk in CT scout views using deep learning
| Autor: | Magdalena Charis Stein, Aydin Demircioglu, Sebastian Blex, Kai Nassenstein, Lale Umutlu, Henrike Geske, Anton S. Quinsten, Moon-Sung Kim |
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| Rok vydání: | 2021 |
| Předmět: |
Adult
Male Science Medizin Computed tomography 02 engineering and technology Pulmonary Artery 030204 cardiovascular system & hematology Radiation Dosage 03 medical and health sciences Deep Learning 0302 clinical medicine Image Processing Computer-Assisted 0202 electrical engineering electronic engineering information engineering Humans Medicine Pulmonary Trunk Bolus tracking Reference standards Aged Retrospective Studies Aged 80 and over Multidisciplinary medicine.diagnostic_test Phantoms Imaging business.industry Scout view Deep learning Angiography Middle Aged Female 020201 artificial intelligence & image processing Artificial intelligence Tomography X-Ray Computed Nuclear medicine business Validation cohort |
| Zdroj: | Scientific Reports, Vol 11, Iss 1, Pp 1-7 (2021) |
| ISSN: | 2045-2322 |
| DOI: | 10.1038/s41598-021-89647-w |
| Popis: | For CT pulmonary angiograms, a scout view obtained in anterior–posterior projection is usually used for planning. For bolus tracking the radiographer manually locates a position in the CT scout view where the pulmonary trunk will be visible in an axial CT pre-scan. We automate the task of localizing the pulmonary trunk in CT scout views by deep learning methods. In 620 eligible CT scout views of 563 patients between March 2003 and February 2020 the region of the pulmonary trunk as well as an optimal slice (“reference standard”) for bolus tracking, in which the pulmonary trunk was clearly visible, was annotated and used to train a U-Net predicting the region of the pulmonary trunk in the CT scout view. The networks’ performance was subsequently evaluated on 239 CT scout views from 213 patients and was compared with the annotations of three radiographers. The network was able to localize the region of the pulmonary trunk with high accuracy, yielding an accuracy of 97.5% of localizing a slice in the region of the pulmonary trunk on the validation cohort. On average, the selected position had a distance of 5.3 mm from the reference standard. Compared to radiographers, using a non-inferiority test (one-sided, paired Wilcoxon rank-sum test) the network performed as well as each radiographer (P |
| Databáze: | OpenAIRE |
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