Deep neural network‐based computer‐assisted detection of cerebral aneurysms in MR angiography

• Published in: Journal of magnetic resonance imaging Vol. 47; no. 4; pp. 948 - 953
• Main Authors: Nakao, Takahiro, Hanaoka, Shouhei, Nomura, Yukihiro, Sato, Issei, Nemoto, Mitsutaka, Miki, Soichiro, Maeda, Eriko, Yoshikawa, Takeharu, Hayashi, Naoto, Abe, Osamu
• Format: Journal Article
• Language: English
• Published: United States Wiley Subscription Services, Inc 01.04.2018
• Subjects: Aneurysm; Aneurysms; Angiography; Artificial neural networks; Brain; cerebral aneurysm; Cerebral Angiography - methods; computer‐assisted detection; convolutional neural network; Female; Field strength; Humans; Image Interpretation, Computer-Assisted - methods; Image Processing, Computer-Assisted; Imaging, Three-Dimensional - methods; Intracranial Aneurysm - diagnostic imaging; Magnetic resonance; Magnetic Resonance Angiography - methods; Magnetic resonance imaging; Male; Medical imaging; Middle Aged; Neural networks; Neuroimaging; Retrospective Studies; Scanners; Sensitivity and Specificity; Statistical analysis; Statistical tests; Training; computer-assisted detection; cerebral aneurysm; convolutional neural network
• ISSN: 1053-1807; 1522-2586; 1522-2586
• DOI: 10.1002/jmri.25842

Background The usefulness of computer‐assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. Purpose To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. Study Type Retrospective study. Subjects There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. Field Strength/Sequence Noncontrast‐enhanced 3D time‐of‐flight (TOF) MRA on 3T MR scanners. Assessment In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. Statistical Tests Free‐response receiver operating characteristic (FROC) analysis. Results Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. Data Conclusion We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. Level of Evidence: 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:948–953.