CT angiography dataset with abdominal aorta segmentation

BACKGROUND: Artificial intelligence algorithms are used to analyze images obtained through radiological diagnostic methods. The effectiveness of such algorithms depends on the availability of relevant and representative training datasets. The volume of such data in the public domain should be increased, particularly datasets containing abdominal aorta computed tomography angiography images, with pathology classification and vessel segmentation. The limitations of existing solutions include small sample sizes, restricted dataset specialization, and inconsistent dataset preparation methodologies.

Aim: To create an open dataset containing computed tomography angiography images of abdominal aorta segmentation for normal aorta, aneurysm, thrombosis, and calcification.

MATERIALS AND METHODS: A technical specification for dataset preparation was developed according to the methodology for testing artificial intelligence algorithms, the required sample size was calculated, and approval was obtained from an independent ethics committee. Regarding dataset creation, a previously developed original semiautomatic segmentation algorithm using Slicer 3D software was employed. The inclusion criteria were computed tomography angiography or abdominal computed tomography scans with contrast, arterial phase, and slice thickness ≤3 mm. Conversely, the exclusion criteria were presence of foreign bodies in the aorta lumen and aortic dissection. The algorithm was tested on patient data obtained from the Unified Radiological Information System. An expert evaluation was conducted to assess the compliance of obtained results with the established requirements and evaluate the time efficiency of using the developed segmentation algorithm.

RESULTS: The calculated sample size was 100 angiographic studies, including arterial phase scans with a slice thickness of ≤1.2 mm. Population data: number of unique patients, 100; percentage of female patients, 51%; and median age, 62 years (age range: 18–84 years). Pathology (including combined pathology) was detected in 61% of cases: 60 studies showed signs of calcification, 18 revealed aortic dilation, and 18 determined signs of thrombosed lumen. The average time to process one study (100 slices) using the developed segmentation algorithm was 0.8 hours.

CONCLUSIONS: A dataset containing 100 computed tomography angiography results with abdominal aorta segmentation for normal cases, aneurysm, thrombosis, and calcification was created. The dataset is publicly available and can be used for developing and testing artificial intelligence algorithms and for anthropomorphic modeling of the abdominal aorta.