Preparation of abdominal computed tomography data set for patients with abdominal aortic aneurysm

Abstract

BACKGROUND: Artificial intelligence (AI) technologies are actively implemented in the processing and analysis of diagnostic medical images. The accuracy and reliability of AI algorithms are determined by the amount and quality of training data sets. Currently, a need exists for increased open access data sets, particularly abdominal aortic CT angiographic studies (CTA). Limitations of existing abdominal aortic CTA data sets are binary labeling (classification of the entire study) and small number of examinations. In addition, most examinations do not contain signs of aortic pathology, which, given its variability, significantly limits their use for AI training, since the target of such algorithms is the detection of pathology. AIM: To prepare a CTA data set for patients with abdominal aortic aneurysm. METHODS: Using the CTA data set with sings of abdominal aortic aneurysm, the stages and features of data set creation for AI training in accordance with the methodological recommendations were considered. Given the basic diagnostic requirements for the selected clinical task, the terms of reference for the preparation of the data set were formed, the required sample size was calculated, and the optimal annotation scenario was determined. The next stage included the selection of initial CT data of abdominal organs in the Unified Radiology Information System, anonymization of data, semi-automatic labeling and of the area of interest (aortic wall and aortic bed) using the 3D Slicer tool and its verification by an examining radiologist, and documentation of intermediate results. RESULTS: The calculated sample volume included 100 scans, containing the arterial phase, with a slice thickness of up to 1.2 mm. The balance of normal vs. pathology classes was chosen to be 1:4. Partial annotation of the data (50%) was performed. CONCLUSIONS: A methodology for preparing CTA data sets was developed. The generated dataset, if the necessary procedures are followed, will be placed in the public domain and may be used for training and testing AI algorithms and conducting scientific research.

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