Automated, anatomy-based, heuristic post-processing reduces false positives and improves interpretability of deep learning intracranial aneurysm detection models.
Authors
Affiliations (8)
Affiliations (8)
- Dept of Radiology, Mass General Brigham, Brigham and Women's Hospital, 75 Francis Street, Boston, MA, 02115, US.
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, US.
- Khoury College of Computer Sciences, Northeastern University, Boston, MA, 02115, US.
- Dept of Imaging, Dana-Farber Cancer Institute, Boston, MA, 02115, US.
- Program in Imaging sciences, Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, USA.
- First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
- Department of Radiology, Harvard Medical School, Boston, MA, 02115, US. [email protected].
- Dept of Imaging, Dana-Farber Cancer Institute, Boston, MA, 02115, US. [email protected].
Abstract
Deep learning (DL) models can help detect intracranial aneurysms on CTA, but high false positive (FP) rates remain a barrier to clinical translation. We developed a fully automated method to reduce FP by integrating automated in-scan anatomic segmentation and removal of background and venous voxels into hybrid heuristic-DL pipelines. Two DL models, CPM-Net, and a deformable 3D convolutional neural network-transformer hybrid (3D-CNN-TR), trained with 1,186 open-source CTAs (1,373 annotated aneurysms) were integrated into an automated pipeline with heuristic post-processing modules comprising 5 combinations of an open-source brain mask and novel DL-based artery-vein separation modules which create artery, vein, and cavernous venous sinus (CVS) segmentation masks from unlabeled CTA data. FPs that do not overlap with the brain mask and/or overlap with vein or vein-more-than-artery masks were eliminated. Each pipeline was tested on 143 held-out private and 843 publicly available CTAs with 218 and 1027 annotated aneurysms, respectively. On our private dataset, CPM-Net yielded 139 true-positives (TP), 79 false-negative (FN), 126 FP, while 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 (combination of brain and vein-more-than-artery mask) performed best, reducing FP by 70.6% (89/126) and 51.6% (94/182) without reducing TP, lowering the FPR from 0.88 to 0.26, and from 1.27 to 0.62 for CPM-Net and 3D-CNN-TR, respectively. On the public RSNA dataset, CPM-Net yielded 791 TP, 236 FN, 748 FP, 0.89 FPR; while 3D-CNN-TR yielded 940 TP, 87 FN, 1552 FP, 1.84 FPR. Method 1 (enhanced brain mask) performed best at preserving TP, removing 1 and 4 TP for CPM-Net and 3D-CNN-TR, respectively, while removing 31.4% (235/748) and 33.8% (524/1552) of FP. Method 5 eliminated 57.9% and 43.8% of FP, but removed 24 and 25 TP from CPM-Net and 3D-CNN-TR output, respectively. Integration of interpretable, anatomy-based, background and vein removal modules into a fully automated DL-based aneurysm detection pipeline improved model performance on two external test datasets. This suggests that heuristic-DL hybrid pipelines created by integrating in-pipeline domain-informed heuristic post-processing with DL may increase performance and clinical acceptance of radiology domain AI.