Takahiro Nakao1, Shouhei Hanaoka2, Yukihiro Nomura3, Issei Sato2,4, Mitsutaka Nemoto3, Soichiro Miki3, Eriko Maeda3, Takeharu Yoshikawa3, Naoto Hayashi3, Osamu Abe1,2. 1. Radiology and Biomedical Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan. 2. Department of Radiology, University of Tokyo Hospital, Tokyo, Japan. 3. Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan. 4. Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan.
Abstract
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.
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.
Authors: Joseph N Stember; Peter Chang; Danielle M Stember; Michael Liu; Jack Grinband; Christopher G Filippi; Philip Meyers; Sachin Jambawalikar Journal: J Digit Imaging Date: 2019-10 Impact factor: 4.056
Authors: Z Shi; B Hu; U J Schoepf; R H Savage; D M Dargis; C W Pan; X L Li; Q Q Ni; G M Lu; L J Zhang Journal: AJNR Am J Neuroradiol Date: 2020-03-12 Impact factor: 3.825