Deep Learning–Based Detection of Intracranial Aneurysms in 3D TOF-MRA

References

1. 1.↵
   2. Vlak MH,
   3. Algra A,
   4. Brandenburg R, et al

   . Prevalence of unruptured intracranial aneurysms, with emphasis on sex, age, comorbidity, country, and time period: a systematic review and meta-analysis. Lancet Neurol 2011;10:626–36 doi:10.1016/S1474-4422(11)70109-0 pmid:21641282

   CrossRefPubMedWeb of Science
2. 2.↵
   2. Kapsalaki EZ,
   3. Rountas CD,
   4. Fountas KN

   . The role of 3 Tesla MRA in the detection of intracranial aneurysms. Int J Vasc Med 2012;2012:792834 doi:10.1155/2012/792834 pmid:22292121

   CrossRefPubMed
3. 3.↵
   2. Nieuwkamp DJ,
   3. Setz LE,
   4. Algra A, et al

   . Changes in case fatality of aneurysmal subarachnoid haemorrhage over time, according to age, sex, and region: a meta-analysis. Lancet Neurol 2009;8:635–42 doi:10.1016/S1474-4422(09)70126-7 pmid:19501022

   CrossRefPubMedWeb of Science
4. 4.↵
   2. Lindgren AE,
   3. Koivisto T,
   4. Björkman J, et al

   . Irregular shape of intracranial aneurysm indicates rupture risk irrespective of size in a population-based cohort. Stroke 2016;47:1219–26 doi:10.1161/STROKEAHA.115.012404 pmid:27073241

   Abstract/FREE Full Text
5. 5.↵
   2. Thompson BG,
   3. Brown RD Jr.,
   4. Amin-Hanjani S, et al

   ; American Heart Association Stroke Council, Council on Cardiovascular and Stroke Nursing, and Council on Epidemiology and Prevention; American Heart Association; American Stroke Association. Guidelines for the management of patients with unruptured intracranial aneurysms: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke 2015;46:2368–400 doi:10.1161/STR.0000000000000070 pmid:26089327

   Abstract/FREE Full Text
6. 6.↵
   2. Wiebers DO,
   3. Whisnant JP,
   4. Huston J 3rd., et al

   ; International Study of Unruptured Intracranial Aneurysms Investigators. Unruptured intracranial aneurysms: natural history, clinical outcome, and risks of surgical and endovascular treatment. Lancet 2003;362:103–10 doi:10.1016/S0140-6736(03)13860-3 pmid:12867109

   CrossRefPubMedWeb of Science
7. 7.↵
   2. Leffers AM,
   3. Wagner A

   . Neurologic complications of cerebral angiography: a retrospective study of complication rate and patient risk factors. Acta Radiol 2000;41:204–10 pmid:10866072

   CrossRefPubMedWeb of Science
8. 8.↵
   2. Wang S,
   3. Summers RM

   . Machine learning and radiology. Med Image Anal 2012;16:933–51 doi:10.1016/j.media.2012.02.005 pmid:22465077

   CrossRefPubMedWeb of Science
9. 9.↵
   2. McDonald RJ,
   3. Schwartz KM,
   4. Eckel LJ, et al

   . The effects of changes in utilization and technological advancements of cross-sectional imaging on radiologist workload. Acad Radiol 2015;22:1191–98 doi:10.1016/j.acra.2015.05.007 pmid:26210525

   CrossRefPubMed
10. 10.↵
    2. Erickson BJ,
    3. Korfiatis P,
    4. Akkus Z, et al

    . Toolkits and libraries for deep learning. J Digit Imaging 2017;30:400–05 doi:10.1007/s10278-017-9965-6 pmid:28315069

    CrossRefPubMed
11. 11.↵
    2. Larson DB,
    3. Chen MC,
    4. Lungren MP, et al

    . Performance of a deep learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 2018;287:313–322 doi:10.1148/radiol.2017170236 pmid:29095675

    CrossRefPubMed
12. 12.↵
    2. Krizhevsky A,
    3. Sutskever I,
    4. Hinton GE

    . ImageNet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 2012:1–9 https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf. Accessed March 20, 2018.
13. 13.↵
    2. Litjens G,
    3. Kooi T,
    4. Bejnordi BE, et al

    . A survey on deep learning in medical image analysis. Med Image Anal 2017;42:60–88 doi:10.1016/j.media.2017.07.005 pmid:28778026

    CrossRefPubMed
14. 14.↵
    2. Miki S,
    3. Hayashi N,
    4. 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;37:1038–43 doi:10.3174/ajnr.A4671 pmid:26892988

    Abstract/FREE Full Text
15. 15.↵
    2. Štepán-Buksakowska IL,
    3. Accurso JM,
    4. Diehn FE, et al

    . Computer-aided diagnosis improves detection of small intracranial aneurysms on MRA in a clinical setting. AJNR Am J Neuroradiol 2014;35:1897–902 doi:10.3174/ajnr.A3996 pmid:24924543

    Abstract/FREE Full Text
16. 16.↵
    2. Yang X,
    3. Blezek DJ,
    4. Cheng LT, et al

    . Computer-aided detection of intracranial aneurysms in MR angiography. J Digit Imaging 2011;24:86–95 doi:10.1007/s10278-009-9254-0 pmid:19937083

    CrossRefPubMed
17. 17.↵
    2. Nakao T,
    3. Hanaoka S,
    4. Nomura Y, et al

    . Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography. J Magn Reson Imaging 2018;47:948–53 doi:10.1002/jmri.25842 pmid:28836310

    CrossRefPubMed
18. 18.↵
    2. Kamnitsas K,
    3. Ledig C,
    4. Newcombe VF, et al

    . Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med Image Anal 2017;36:61–78 doi:10.1016/j.media.2016.10.004 pmid:27865153

    CrossRefPubMed
19. 19.↵
    2. Kamnitsas K

    . GitHub-Kamnitsask/deepmedic. Efficient Multi-Scale 3D Convolutional Neural Network for Brain Lesion Segmentation. Published 2017. https://github.com/Kamnitsask/deepmedic. Accessed March 20, 2018.
20. 20.↵
    2. Jenkinson M,
    3. Pechaud M,
    4. Smith S

    . BET2: MR-based estimation of brain, skull and scalp surfaces. In: Eleventh Annual Meeting of the Organization for Human Brain Mapping, Toronto, Ontario, Canada, June 12–16, 2005
21. 21.↵
    2. Tustison NJ,
    3. Avants BB,
    4. Cook PA, et al

    . N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310–20 doi:10.1109/TMI.2010.2046908 pmid:20378467

    CrossRefPubMedWeb of Science
22. 22.↵
    2. Yushkevich PA,
    3. Piven J,
    4. Hazlett HC, et al

    . User-guided 3D active contour segmentation of anatomical structures: significantly improved efficiency and reliability. Neuroimage 2006;31:1116–28 doi:10.1016/j.neuroimage.2006.01.015 pmid:16545965

    CrossRefPubMedWeb of Science
23. 23.↵
    2. Kamnitsas K,
    3. Chen LC,
    4. Ledig C, et al

    . Ischemic stroke lesion segmentation. In: Proceedings of MICCAI-ISLES, Munich, Germany. October 5, 2015. 2015:13–16. www.isles-challenge.org. Accessed March 19, 2018.
24. 24.↵
    2. Taha AA,
    3. Hanbury A

    . Metrics for evaluating 3D medical image segmentation: analysis, selection, and tool. BMC Med Imaging 2015;15:29 doi:10.1186/s12880-015-0068-x pmid:26263899

    CrossRefPubMed
25. 25.↵
    2. Tustison NJ,
    3. Avants BB,
    4. Cook PA, et al

    . N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 2010;29:1310–20 doi:10.1109/TMI.2010.2046908 pmid:20378467

    CrossRefPubMedWeb of Science
26. 26.↵
    2. Li M,
    3. Zhu Y,
    4. Song H, et al

    . Subarachnoid hemorrhage in patients with good clinical grade: accuracy of 3.0-T MR angiography for detection and characterization. Radiology 2017;284:191–99 doi:10.1148/radiol.2017161469 pmid:28234561

    CrossRefPubMed
27. 27.↵
    2. MacDonald SL,
    3. Cowan IA,
    4. Floyd R, et al

    . Measuring and managing radiologist workload: application of lean and constraint theories and production planning principles to planning radiology services in a major tertiary hospital. J Med Imaging Radiat Oncol 2013;57:544–50 doi:10.1111/1754-9485.12090 pmid:24119267

    CrossRefPubMed
28. 28.↵
    2. Berbaum KS,
    3. Franken EA Jr.,
    4. Dorfman DD, et al

    . Satisfaction of search in diagnostic radiology. Invest Radiol 1990;25:133–40 doi:10.1097/00004424-199002000-00006 pmid:2312249

    CrossRefPubMedWeb of Science
29. 29.↵
    2. Mazzara GP,
    3. Velthuizen RP,
    4. Pearlman JL, et al

    . Brain tumor target volume determination for radiation treatment planning through automated MRI segmentation. Int J Radiat Oncol Biol Phys 2004;59:300–12 doi:10.1016/j.ijrobp.2004.01.026 pmid:15093927

    CrossRefPubMedWeb of Science
30. 30.↵
    2. Nomura Y,
    3. Masutani Y,
    4. 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 doi:10.5430/jbgc.v4n4p12

    CrossRef
31. 31.↵
    2. Kalavathi P,
    3. Prasath VB

    . Methods on skull stripping of MRI head scan images: a review. J Digit Imaging 2016;29:365–79 doi:10.1007/s10278-015-9847-8 pmid:26628083

    CrossRefPubMed
32. 32.↵
    2. Kamnitsas K

    . DeepMedic GitHub documentation. Documentation about pretraining/finetuning. Published 2017. https://github.com/Kamnitsask/deepmedic/blob/master/documentation/dmRes.png. Accessed July 30, 2018.