Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography

• URL: http://arxiv.org/abs/2005.11098v2
• Date: Tue, 14 Dec 2021 20:03:17 GMT
• Title: Deep Learning Based Detection and Localization of Intracranial Aneurysms in Computed Tomography Angiography
• Authors: Dufan Wu, Daniel Montes, Ziheng Duan, Yangsibo Huang, Javier M. Romero, Ramon Gilberto Gonzalez, Quanzheng Li
• Abstract summary: A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-positive reduction. Our model showed statistically higher accuracy, sensitivity, and specificity when compared to the available model at 0.25 FPPV and the best F-1 score.
• Score: 5.973882600944421
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Purpose: To develop CADIA, a supervised deep learning model based on a region proposal network coupled with a false-positive reduction module for the detection and localization of intracranial aneurysms (IA) from computed tomography angiography (CTA), and to assess our model's performance to a similar detection network. Methods: In this retrospective study, we evaluated 1,216 patients from two separate institutions who underwent CT for the presence of saccular IA>=2.5 mm. A two-step model was implemented: a 3D region proposal network for initial aneurysm detection and 3D DenseNetsfor false-positive reduction and further determination of suspicious IA. Free-response receiver operative characteristics (FROC) curve and lesion-/patient-level performance at established false positive per volume (FPPV) were also performed. Fisher's exact test was used to compare with a similar available model. Results: CADIA's sensitivities at 0.25 and 1 FPPV were 63.9% and 77.5%, respectively. Our model's performance varied with size and location, and the best performance was achieved in IA between 5-10 mm and in those at anterior communicating artery, with sensitivities at 1 FPPV of 95.8% and 94%, respectively. Our model showed statistically higher patient-level accuracy, sensitivity, and specificity when compared to the available model at 0.25 FPPV and the best F-1 score (P<=0.001). At 1 FPPV threshold, our model showed better accuracy and specificity (P<=0.001) and equivalent sensitivity. Conclusions: CADIA outperformed a comparable network in the detection task of IA. The addition of a false-positive reduction module is a feasible step to improve the IA detection models.

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