Details

Autor(en) / Beteiligte

• Ivantsits, Matthias
• Goubergrits, Leonid
• Kuhnigk, Jan-Martin
• Huellebrand, Markus
• Bruening, Jan
• Kossen, Tabea
• Pfahringer, Boris
• Schaller, Jens
• Spuler, Andreas
• Kuehne, Titus
• Jia, Yizhuan
• Li, Xuesong
• Shit, Suprosanna
• Menze, Bjoern
• Su, Ziyu
• Ma, Jun
• Nie, Ziwei
• Jain, Kartik
• Liu, Yanfei
• Lin, Yi
• Hennemuth, Anja

Titel

Detection and analysis of cerebral aneurysms based on X-ray rotational angiography - the CADA 2020 challenge

Ist Teil von

• Medical image analysis, 2022-04, Vol.77, p.102333-102333, Article 102333

Ort / Verlag

Netherlands: Elsevier B.V

Erscheinungsjahr

2022

Quelle

MEDLINE

Beschreibungen/Notizen

• •The Cranial Aneurysm Detection and Analysis Challenge (CADA) provides a benchmarking dataset (3D rotational X-ray angiography) and evaluation framework for: (a) Aneurysm detection, (b) Aneurysm segmentation, (c) Rupture risk estimation.•The paper provides a comparison of image-based aneurysm rupture risk assessment approaches from different domains such as (a) Weighted parameter combinations (clinical score), (b) Radiomics-based machine learning.(c) Computation fluid dynamics.•The paper assesses the performance of a fully automatic pipeline for the detection and rupture risk assessment. [Display omitted] The Cerebral Aneurysm Detection and Analysis (CADA) challenge was organized to support the development and benchmarking of algorithms for detecting, analyzing, and risk assessment of cerebral aneurysms in X-ray rotational angiography (3DRA) images. 109 anonymized 3DRA datasets were provided for training, and 22 additional datasets were used to test the algorithmic solutions. Cerebral aneurysm detection was assessed using the F2 score based on recall and precision, and the fit of the delivered bounding box was assessed using the distance to the aneurysm. The segmentation quality was measured using the Jaccard index and a combination of different surface distance measures. Systematic errors were analyzed using volume correlation and bias. Rupture risk assessment was evaluated using the F2 score. 158 participants from 22 countries registered for the CADA challenge. The U-Net-based detection solutions presented by the community show similar accuracy compared to experts (F2 score 0.92), with a small number of missed aneurysms with diameters smaller than 3.5 mm. In addition, the delineation of these structures, based on U-Net variations, is excellent, with a Jaccard score of 0.92. The rupture risk estimation methods achieved an F2 score of 0.71. The performance of the detection and segmentation solutions is equivalent to that of human experts. The best results are obtained in rupture risk estimation by combining different image-based, morphological, and computational fluid dynamic parameters using machine learning methods. Furthermore, we evaluated the best methods pipeline, from detecting and delineating the vessel dilations to estimating the risk of rupture. The chain of these methods achieves an F2-score of 0.70, which is comparable to applying the risk prediction to the ground-truth delineation (0.71).