Automated anatomy-based post-processing reduces false positives and improved interpretability of deep learning intracranial aneurysm detection
Journal:
arXiv
Published Date:
Jul 1, 2025
Abstract
Introduction: Deep learning (DL) models can help detect intracranial
aneurysms on CTA, but high false positive (FP) rates remain a barrier to
clinical translation, despite improvement in model architectures and strategies
like detection threshold tuning. We employed an automated, anatomy-based,
heuristic-learning hybrid artery-vein segmentation post-processing method to
further reduce FPs. Methods: Two DL models, CPM-Net and a deformable 3D
convolutional neural network-transformer hybrid (3D-CNN-TR), were trained with
1,186 open-source CTAs (1,373 annotated aneurysms), and evaluated with 143
held-out private CTAs (218 annotated aneurysms). Brain, artery, vein, and
cavernous venous sinus (CVS) segmentation masks were applied to remove possible
FPs in the DL outputs that overlapped with: (1) brain mask; (2) vein mask; (3)
vein more than artery masks; (4) brain plus vein mask; (5) brain plus vein more
than artery masks. Results: CPM-Net yielded 139 true-positives (TP); 79
false-negative (FN); 126 FP. 3D-CNN-TR yielded 179 TP; 39 FN; 182 FP. FPs were
commonly extracranial (CPM-Net 27.3%; 3D-CNN-TR 42.3%), venous (CPM-Net 56.3%;
3D-CNN-TR 29.1%), arterial (CPM-Net 11.9%; 3D-CNN-TR 53.3%), and non-vascular
(CPM-Net 25.4%; 3D-CNN-TR 9.3%) structures. Method 5 performed best, reducing
CPM-Net FP by 70.6% (89/126) and 3D-CNN-TR FP by 51.6% (94/182), without
reducing TP, lowering the FP/case rate from 0.88 to 0.26 for CPM-NET, and from
1.27 to 0.62 for the 3D-CNN-TR. Conclusion: Anatomy-based, interpretable
post-processing can improve DL-based aneurysm detection model performance. More
broadly, automated, domain-informed, hybrid heuristic-learning processing holds
promise for improving the performance and clinical acceptance of aneurysm
detection models.
