Original Research

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

Corresponding Author

Takahiro Nakao MD

[email protected]

orcid.org/0000-0001-9498-7501

Radiology and Biomedical Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

Address reprint requests to: T.N., Department of Radiology, University of Tokyo Hospital, Tokyo, Japan. E-mail: [email protected]Search for more papers by this author

Shouhei Hanaoka MD, PhD,

Shouhei Hanaoka MD, PhD

Department of Radiology, University of Tokyo Hospital, Tokyo, Japan

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Yukihiro Nomura PhD, RT,

Yukihiro Nomura PhD, RT

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Issei Sato PhD,

Issei Sato PhD

Department of Radiology, University of Tokyo Hospital, Tokyo, Japan

Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan

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Mitsutaka Nemoto PhD,

Mitsutaka Nemoto PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Soichiro Miki MD, PhD,

Soichiro Miki MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Eriko Maeda MD, PhD,

Eriko Maeda MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Takeharu Yoshikawa MD, PhD,

Takeharu Yoshikawa MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Naoto Hayashi MD, PhD,

Naoto Hayashi MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Osamu Abe MD, PhD,

Osamu Abe MD, PhD

Radiology and Biomedical Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

Department of Radiology, University of Tokyo Hospital, Tokyo, Japan

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Takahiro Nakao MD,

Corresponding Author

Takahiro Nakao MD

[email protected]

orcid.org/0000-0001-9498-7501

Radiology and Biomedical Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

Address reprint requests to: T.N., Department of Radiology, University of Tokyo Hospital, Tokyo, Japan. E-mail: [email protected]Search for more papers by this author

Shouhei Hanaoka MD, PhD,

Shouhei Hanaoka MD, PhD

Department of Radiology, University of Tokyo Hospital, Tokyo, Japan

Search for more papers by this author

Yukihiro Nomura PhD, RT,

Yukihiro Nomura PhD, RT

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Issei Sato PhD,

Issei Sato PhD

Department of Radiology, University of Tokyo Hospital, Tokyo, Japan

Graduate School of Frontier Sciences, University of Tokyo, Tokyo, Japan

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Mitsutaka Nemoto PhD,

Mitsutaka Nemoto PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Soichiro Miki MD, PhD,

Soichiro Miki MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Eriko Maeda MD, PhD,

Eriko Maeda MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Takeharu Yoshikawa MD, PhD,

Takeharu Yoshikawa MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Naoto Hayashi MD, PhD,

Naoto Hayashi MD, PhD

Department of Computational Diagnostic Radiology and Preventive Medicine, University of Tokyo Hospital, Tokyo, Japan

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Osamu Abe MD, PhD,

Osamu Abe MD, PhD

Radiology and Biomedical Engineering, Graduate School of Medicine, University of Tokyo, Tokyo, Japan

Department of Radiology, University of Tokyo Hospital, Tokyo, Japan

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First published: 24 August 2017

https://doi.org/10.1002/jmri.25842

Citations: 159

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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.

References

1 Suarez JI, Tarr RW, Selman WR. Aneurysmal subarachnoid hemorrhage. N Engl J Med 2006; 354: 387–396.
10.1056/NEJMra052732
CAS PubMed Web of Science® Google Scholar
2 Miki S, Hayashi N, Masutani Y, et al. Computer-assisted detection of cerebral aneurysms in MR angiography in a routine Image-reading environment: Effects on diagnosis by radiologists. AJNR Am J Neuroradiol 2016; 37: 1038–1043.
10.3174/ajnr.A4671
CAS PubMed Web of Science® Google Scholar
3 LeCun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436–444.
10.1038/nature14539
CAS PubMed Web of Science® Google Scholar
4 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of NIPS 2012. Lake Tahoe, NV: MIT Press; 2012; 1097–1105.
Google Scholar
5 Hinton G, Deng L, Yu D, et al. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process Mag 2012; 29: 82–97.
10.1109/MSP.2012.2205597
Web of Science® Google Scholar
6 Collobert R, Weston J, Bottou L, et al. Natural language processing (almost) from scratch. J Mach Learn Res 2011; 12: 2493–2537.
Web of Science® Google Scholar
7 Roth HR, Lu L, Seff A, et al. A new 2.5D representation for lymph node detection using random sets of deep convolutional neural network observations. Med Image Comput Comput Assist Interv 2014; 17(Pt 1): 520–527.
PubMed Google Scholar
8 Tajbakhsh N, Gotway MB, Liang J. Computer-aided pulmonary embolism detection using a novel vessel-aligned multi-planar image representation and convolutional neural networks. Med Image Comput Comput Assist Interv 2015; 18(Pt 2): 62–69.
Google Scholar
9 Dou Q, Chen H, Yu L, et al. Automatic detection of cerebral microbleeds from MR images via 3D convolutional neural networks. IEEE Trans Med Imaging 2016; 35: 1182–1195.
10.1109/TMI.2016.2528129
PubMed Web of Science® Google Scholar
10 Prasoon A, Petersen K, Igel C, Lauze F, Dam E, Nielsen M. Deep feature learning for knee cartilage segmentation using a triplanar convolutional neural network. Med Image Comput Comput Assist Interv 2013; 16(Pt 2): 246–253.
PubMed Google Scholar
11 Milletari F, Navab N, Ahmadi S-A. V-Net: fully convolutional neural networks for volumetric medical image segmentation. 2016;arXiv:1606.04797v1.
Google Scholar
12 Hubel DH, Wiesel TN. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex. J Physiol 1962; 160: 106–154.
10.1113/jphysiol.1962.sp006837
CAS PubMed Web of Science® Google Scholar
13 Yang X, Blezek DJ, Cheng LTE, et al. Computer-aided detection of intracranial aneurysms in MR angiography. J Digit Imaging 2011; 24: 86–95.
10.1007/s10278-009-9254-0
PubMed Web of Science® Google Scholar
14 Nomura Y, Masutani Y, Miki S, et al. Performance improvement in computerized detection of cerebral aneurysms by retraining classifier using feedback data collected in routine reading environment. J Biomed Graph Comput 2014; 4: 12–21.
Google Scholar
15 Hanaoka S, Nomura Y, Nemoto M, et al. HoTPiG: A novel geometrical feature for vessel morphometry and its application to cerebral aneurysm detection. Med Image Comput Comput Assist Interv 2015; 18(Pt 2): 103–110.
Google Scholar
16 van Ginneken B, Schaefer-Prokop CM, Prokop M. Computer-aided diagnosis: how to move from the laboratory to the clinic. Radiology 2011; 261: 719–732.
10.1148/radiol.11091710
PubMed Web of Science® Google Scholar
17 Štepán-Buksakowska IL, Accurso JM, Diehn FE, et al. Computer-aided diagnosis improves detection of small intracranial aneurysms on MRA in a clinical setting. AJNR Am J Neuroradiol 2014; 35: 1897–1902.
10.3174/ajnr.A3996
CAS PubMed Web of Science® Google Scholar
18 Nyúl LG, Udupa JK. On standardizing the MR image intensity scale. Magn Reson Med 1999; 42: 1072–1081.
10.1002/(SICI)1522-2594(199912)42:6<1072::AID-MRM11>3.0.CO;2-M
CAS PubMed Web of Science® Google Scholar
19 Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift. 2015;arXiv:1502.03167v3.
Google Scholar
20 Kingma D, Ba J. Adam: A method for stochastic optimization. 2014;arXiv:1412.6980v8.
Google Scholar
21 Bunch PC, Hamilton JF, Sanderson GK, Simmons AH. A free response approach to the measurement and characterization of radiographic observer performance. In: Proceedings of SPIE 1977. Bellingham, WA: Society of Photo-Optical Instrumentation Engineers; 1977; 127: 124–135.
10.1117/12.955926
Google Scholar
22 Bergstra J, Bengio Y. Random search for hyper-parameter optimization. J Mach Learn Res 2012; 13: 281–305.
Web of Science® Google Scholar
23 Nomura Y, Hayashi N, Masutani Y, et al. CIRCUS: an MDA platform for clinical image analysis in hospitals. Trans Mass-Data Anal Images Signals 2010; 2: 112–127.
Google Scholar

Citing Literature

Volume47, Issue4

April 2018

Pages 948-953

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

Abstract

Background

Purpose

Study Type

Subjects

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

References

Citing Literature

References

Information

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

Abstract

Background

Purpose

Study Type

Subjects

Field Strength/Sequence

Assessment

Statistical Tests

Results

Data Conclusion

References

Citing Literature

References

Related

Information