E-024 Deep learning-based cerebral aneurysm segmentation and morphological analysis on the three-dimensional rotational angiography. (23rd July 2022)

• Record Type: Journal Article
• Authors: Nishi, H; Lustici, A; Cancelliere, N; Marotta, T; Spears, J; Pereira, V
• Is Part Of: Journal of neurointerventional surgery. Volume 14(2022)Supplement 1
• Page Start: A87
• Page End: A88
• Publication Date: 2022-07-23
• Subjects: Nervous system -- Surgery -- Periodicals; Cerebrovascular disease -- Surgery -- Periodicals; 617.48
• DOI: 10.1136/neurintsurg-2022-SNIS.135
• Languages: English
• ISSNs: 1759-8478

Abstract

Abstract : Background: Morphological assessment of cerebral aneurysms based on cerebral angiography is an essential step when planning strategy and device selection in the endovascular treatment of cerebral aneurysms, but manual evaluation by human raters has only moderate inter-/intra-rater reliability. Purpose: To develop and evaluate the performance of an automatic morphological analysis tool for cerebral aneurysms, which is based on a combination of deep learning and rule-based image processing algorithms. Materials and Methods: Cerebral angiography data from 889 consecutive patients with suspected cerebral aneurysms were retrospectively collected at our institution from January 2017 to October 2021. The automatic morphological analysis model was trained and developed on the derivation cohort dataset consisting of 388 scans with 437 aneurysms, and the performance of the model was tested on the validation cohort dataset consisting of 96 scans with 124 aneurysms. Five clinically important parameters were automatically calculated by the model; aneurysm volume, maximum aneurysm size, neck size, aneurysm height, and aspect ratio. Results: On the validation cohort dataset, the average aneurysm size was 7.9±4.6 mm. The proposed model displayed high detection and segmentation accuracy with lesion-level sensitivity of 98.4%, false positives per scan of 0.21, and mean Dice similarity index of 0.87 (median 0.93). All the morphological parameters were significantly correlated with the reference standard (all p<0.0001; Pearson correlation analysis). Among the aneurysms, the model could discriminate aneurysms smaller than 7 mm with sensitivity 90.7%, specificity 100.0%, and area under curve of 0.95, and wide-neck aneurysms (neck size larger than 4 mm) with sensitivity 85.7%, specificity 88.5%, and area under curve of 0.87. Conclusions: The automatic aneurysm analysis model based on angiography data had high accuracy on evaluating the morphological characteristics of cerebral aneurysms, which might help planning strategy and selecting devices for endovascular treatment of cerebral aneurysms. Disclosures: H. Nishi: None. A. Lustici: None. N. Cancelliere: None. T. Marotta: None. J. Spears: None. V. Pereira: None.