An open-source, automated machine learning approach for large-scale image retrieval for thoracic aorta analysis
Brian C Ayers1, Aaron D Aguirre2,3, Thoralf M Sundt1
1Division of Cardiac Surgery, Massachusetts General Hospital, Boston, MA 02114, United States.
Objective:
To develop an image retrieval pipeline capable of identifying specific series of thoracic aortic computed tomography (CT) scans from a diverse database.
Materials And Methods:
An automated image analysis pipeline was developed to select series that show the entire thoracic aorta with arterial phase contrast from within a heterogeneous institutional cohort of 4184 CT scans of the chest.
Results:
The automated pipeline identified 3435 (82%) studies from within the database that met criteria. Manual review confirmed 99.1% of the selected scans were accurately selected, and 93.6% of excluded scans were appropriately excluded.
Discussion And Conclusion:
We present an open-source, image-retrieval pipeline that, with a high degree of accuracy, can identify aortic imaging studies that meet specific criteria from within a heterogeneous collection of images. This pipeline serves as a framework that can be easily modified for other clinical use cases and can be deployed across multiple centers to promote multi-institutional research.
