Deep Learning–Based Automatic Segmentation of Lumbosacral Nerves on CT for Spinal Intervention: A Translational Study

REFERENCES

1. 1.↵
   2. Mokdad AH,
   3. Ballestros K,
   4. Echko M, et al

   ; US Burden of Disease Collaborators. The state of US Health, 1990–2016: burden of diseases, injuries, and risk factors among US states. JAMA 2018;319:1444–72 doi:10.1001/jama.2018.0158 pmid:29634829

   CrossRefPubMed
2. 2.↵
   2. Buchbinder R,
   3. van Tulder M,
   4. Oberg B, et al

   ; Lancet Low Back Pain Series Working Group. Low back pain: a call for action. Lancet 2018;391:2384–88 doi:10.1016/S0140-6736(18)30488-4 pmid:29573871

   CrossRefPubMed
3. 3.↵
   2. Iannuccilli JD,
   3. Prince EA,
   4. Soares GM

   . Interventional spine procedures for management of chronic low back pain-a primer. Semin Intervent Radiol 2013;30:307–17 doi:10.1055/s-0033-1353484 pmid:24436553

   CrossRefPubMed
4. 4.↵
   2. ÖKmen K,
   3. ÖKmen BM

   . The efficacy of interlaminar epidural steroid administration in multilevel intervertebral disc disease with chronic low back pain: a randomized, blinded, prospective study. Spine J 2017;17:168–74 doi:10.1016/j.spinee.2016.08.024 pmid:27555486

   CrossRefPubMed
5. 5.↵
   2. Cohen SP,
   3. Hanling S,
   4. Bicket MC, et al

   . Epidural steroid injections compared with gabapentin for lumbosacral radicular pain: multicenter randomized double blind comparative efficacy study. BMJ 2015;350:h1748 doi:10.1136/bmj.h1748 pmid:25883095

   Abstract/FREE Full Text
6. 6.↵
   2. Lee JH,
   3. Shin KH,
   4. Bahk SJ, et al

   . Comparison of clinical efficacy of transforaminal and caudal epidural steroid injection in lumbar and lumbosacral disc herniation: a systematic review and meta-analysis. Spine J 2018;18:2343–53 doi:10.1016/j.spinee.2018.06.720 pmid:30030083

   CrossRefPubMed
7. 7.↵
   2. Wei G,
   3. Liang J,
   4. Chen B, et al

   . Comparison of transforaminal verse interlaminar epidural steroid injection in low back pain with lumbosacral radicular pain: a meta-analysis of the literature. Int Orthop 2016;40:2533–45 doi:10.1007/s00264-016-3220-5 pmid:27198870

   CrossRefPubMed
8. 8.↵
   2. Glaser SE,
   3. Shah RV

   . Root cause analysis of paraplegia following transforaminal epidural steroid injections: the ‘unsafe’ triangle. Pain Physician 2010;13:237–44 pmid:20495587

   PubMedWeb of Science
9. 9.↵
   2. Tumialán LM,
   3. Madhavan K,
   4. Godzik J, et al

   . The history of and controversy over Kambin's triangle: a historical analysis of the lumbar transforaminal corridor for endoscopic and surgical approaches. World Neurosurg 2019;123:402–08 doi:10.1016/j.wneu.2018.10.221 pmid:30415041

   CrossRefPubMed
10. 10.↵
    2. Ra IH,
    3. Min WK

    . Optimal angle of needle insertion for fluoroscopy-guided transforaminal epidural injection of L5. Pain Pract 2015;15:393–99 doi:10.1111/papr.12187 pmid:24690186

    CrossRefPubMed
11. 11.↵
    2. Surange PN

    . S1 Transforaminal: Technique and Troubleshooting. 2016. https://www.researchgate.net/publication/303324602_S1_Transforaminal_Technique_and_Troubleshooting. Accessed January 1, 2016.
12. 12.↵
    2. Chen X,
    3. Cheng J,
    4. Gu X, et al

    . Development of preoperative planning software for transforaminal endoscopic surgery and the guidance for clinical applications. Int J Comput Assist Radiol Surg 2016;11:613–20 doi:10.1007/s11548-015-1282-2 pmid:26450110

    CrossRefPubMed
13. 13.↵
    2. Boswell MV,
    3. Trescot AM,
    4. Datta S, et al

    ; American Society of Interventional Pain Physicians. Interventional techniques: evidence-based practice guidelines in the management of chronic spinal pain. Pain Physician 2007;10:7–111 pmid:17256025

    PubMed
14. 14.↵
    2. Wilkinson I,
    3. Cohen SP

    . Epidural steroids for spinal pain and radiculopathy: a narrative, evidence-based review. Curr Opin Anaesthesiol 2013;26:562–72 doi:10.1097/ACO.0b013e3283628e87 pmid:23787490

    CrossRefPubMed
15. 15.↵
    2. Kim WJ,
    3. Shin HY,
    4. Yoo SH, et al

    . Comparison of epidural spreading patterns and clinical outcomes of transforaminal epidural steroid injection with high-volume injectate via the subpedicular versus the retrodiscal approach. Pain Physician 2018;21:269–78 pmid:29871371

    PubMed
16. 16.↵
    2. Park KD,
    3. Lee J,
    4. Jee H, et al

    . Kambin triangle versus the supraneural approach for the treatment of lumbar radicular pain. Am J Phys Med Rehabil 2012;91:1039–50 doi:10.1097/PHM.0b013e318264573a pmid:22854909

    CrossRefPubMed
17. 17.↵
    2. Fenster AJ,
    3. Fernandes K,
    4. Brook AL, et al

    . The safety of CT-guided epidural steroid injections in an older patient cohort. Pain Physician 2016;19:E1139–46 pmid:27906944

    PubMed
18. 18.↵
    2. Timpone VM,
    3. Hirsch JA,
    4. Gilligan CJ, et al

    . Computed tomography guidance for spinal intervention: basics of technique, pearls, and avoiding pitfalls. Pain Physician 2013;16:369–77 pmid:23877453

    PubMed
19. 19.↵
    2. Han Z,
    3. Wei B,
    4. Leung S, et al

    . Automated pathogenesis-based diagnosis of lumbar neural foraminal stenosis via deep multiscale multitask learning. Neuroinformatics 2018;16:325–37 doi:10.1007/s12021-018-9365-1 pmid:29450848

    CrossRefPubMed
20. 20.↵
    2. Pesteie M,
    3. Lessoway V,
    4. Abolmaesumi P, et al

    . Automatic localization of the needle target for ultrasound-guided epidural injections. IEEE Trans Med Imaging 2018;37:81–92 doi:10.1109/TMI.2017.2739110 pmid:28809679

    CrossRefPubMed
21. 21.↵
    2. Yasaka K,
    3. Akai H,
    4. Abe O, et al

    . Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study. Radiology 2018;286:887–96 doi:10.1148/radiol.2017170706 pmid:29059036

    CrossRefPubMed
22. 22.↵
    2. Lu JT,
    3. Pedemonte S,
    4. Bizzo B, et al

    . DeepSPINE: automated lumbar vertebral segmentation, disc-level designation, and spinal stenosis grading using deep learning. 2018. https://arxiv.org/abs/1807.10215. Accessed July 26, 2018.
23. 23.↵
    2. Mazurowski MA,
    3. Buda M,
    4. Saha A, et al

    . Deep learning in radiology: an overview of the concepts and a survey of the state of the art. J Magn Reson Imaging 2019;49:939–94 doi:10.1002/jmri.26534 pmid:30575178

    CrossRefPubMed
24. 24.↵
    2. Ronneberger O,
    3. Fischer P,
    4. Brox T

    . U-Net: convolutional networks for biomedical image segmentation. 2015. https://arxiv.org/abs/1505.04597. Accessed November 18, 2015.
25. 25.↵
    2. Ö Çiçek ,
    3. Abdulkadir A,
    4. Lienkamp SS, et al

    . 3D U-Net: learning dense volumetric segmentation from sparse annotation. 2016. https://arxiv.org/abs/1606.06650. Accessed October 2, 2016.
26. 26.↵
    2. Wang C,
    3. Macgillivray T,
    4. Macnaught G, et al

    . A two-stage 3D Unet framework for multi-class segmentation on full resolution image. 2018. https://arxiv.org/pdf/1804.04341.pdf. Accessed April 12, 2018.
27. 27.↵
    2. Huang Q,
    3. Sun J,
    4. Ding H, et al

    . Robust liver vessel extraction using 3D U-Net with variant dice loss function. Comput Biol Med 2018;101:153–62 doi:10.1016/j.compbiomed.2018.08.018 pmid:30144657

    CrossRefPubMed
28. 28.↵
    2. Funke J,
    3. Tschopp FD,
    4. Grisaitis W, et al

    . Large scale image segmentation with structured loss based deep learning for connectome reconstruction. IEEE Trans Pattern Anal Mach Intel 2018 May 24. [Epub ahead of print] doi:10.1109/TPAMI.2018.2835450 pmid:29993708

    CrossRefPubMed
29. 29.↵
    2. Fedorov A,
    3. Beichel R,
    4. Kalpathy-Cramer J, et al

    . 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 2012;30:1323–41 doi:10.1016/j.mri.2012.05.001 pmid:22770690

    CrossRefPubMedWeb of Science
30. 30.↵
    2. Zou KH,
    3. Warfield SK,
    4. Bharatha A, et al

    . Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 2004;11:178–89 doi:10.1016/S1076-6332(03)00671-8 pmid:14974593

    CrossRefPubMedWeb of Science
31. 31.↵
    2. He K,
    3. Zhang X,
    4. Ren S, et al

    . Delving deep into rectifiers: surpassing human-level performance on ImageNet classification. 2015. https://arxiv.org/abs/1502.01852. Accessed February 6, 2015.
32. 32.↵
    2. Kingma DP,
    3. Ba J

    . Adam: a method for stochastic optimization. Computer Science 2014. https://arxiv.org/abs/1412.6980. Accessed December 22, 2014.
33. 33.↵
    2. Cai Y,
    3. Osman S,
    4. Sharma M, et al

    . Multi-modality vertebra recognition in arbitrary views using 3D deformable hierarchical model. IEEE Trans Med Imaging 2015;34:1676–93 doi:10.1109/TMI.2015.2392054 pmid:25594966

    CrossRefPubMed
34. 34.↵
    2. Mandell JC,
    3. Czuczman GJ,
    4. Gaviola GC, et al

    . The lumbar neural foramen and transforaminal epidural steroid injections: an anatomic review with key safety considerations in planning the percutaneous approach. AJR Am J Roentgenol 2017;209:W1–10 doi:10.2214/AJR.16.16981 pmid:28504548

    CrossRefPubMed
35. 35.↵
    2. Kim C,
    3. Choi HE,
    4. Kang S

    . Contrast spreading patterns in retrodiscal transforaminal epidural steroid injection. Ann Rehabil Med 2012;36:474–79 doi:10.5535/arm.2012.36.4.474 pmid:22977772

    CrossRefPubMed
36. 36.↵
    2. Hardenbrook M,
    3. Lombardo S,
    4. Wilson MC, et al

    . The anatomic rationale for transforaminal endoscopic interbody fusion: a cadaveric analysis. Neurosurg Focus 2016;40:E12 doi:10.3171/2015.10.FOCUS15389 pmid:26828881

    CrossRefPubMed
37. 37.↵
    2. Guan X,
    3. Gu X,
    4. Zhang L, et al

    . Morphometric analysis of the working zone for posterolateral endoscopic lumbar discectomy based on magnetic resonance neurography. J Spinal Disord Tech 2015;28:E78–84 doi:10.1097/BSD.0000000000000145 pmid:25093650

    CrossRefPubMed
38. 38.↵
    2. Mandell JC,
    3. Czuczman GJ,
    4. Gaviola GC, et al

    . The lumbar neural foramen and transforaminal epidural steroid injections: an anatomic review with key safety considerations in planning the percutaneous approach. AJR Am J Roentgenol 2017;209:W26–35 doi:10.2214/AJR.16.17471 pmid:28504548

    CrossRefPubMed
39. 39.↵
    2. Guan X,
    3. Fan G,
    4. Wu X, et al

    . Diffusion tensor imaging studies of cervical spondylotic myelopathy: a systemic review and meta-analysis. PLoS One 2015;10:e0117707 doi:10.1371/journal.pone.0117707 pmid:25671624

    CrossRefPubMed
40. 40.↵
    2. Janssens R,
    3. Zeng G,
    4. Zheng G

    . Fully automatic segmentation of lumbar vertebrae from CT images using cascaded 3D fully convolutional networks. 2018. https://www.researchgate.net/publication/325516176_Fully_automatic_segmentation_of_lumbar_vertebrae_from_CT_images_using_cascaded_3D_fully_convolutional_networks. Accessed December 5, 2017.
41. 41.↵
    2. Dong H,
    3. Liu F,
    4. Yang G, et al

    . Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks. 2017. https://www.researchgate.net/publication/318168153_Automatic_Brain_Tumor_Detection_and_Segmentation_Using_U-Net_Based_Fully_Convolutional_Networks. Accessed June 22, 2017.
42. 42.↵
    2. Feng X,
    3. Qing K,
    4. Tustison NJ, et al

    . Deep convolutional neural network for segmentation of thoracic organs-at-risk using cropped 3D images. Med Phys 2019 Mar 4. [Epub ahead of print] doi:10.1002/mp.13466 pmid:30830685

    CrossRefPubMed
43. 43.↵
    2. Novikov AA,
    3. Major D,
    4. Wimmer M, et al

    . Deep sequential segmentation of organs in volumetric medical scans. IEEE Trans Med Imaging 2018 Nov 16. [Epub ahead of print] doi:10.1109/TMI.2018.2881678 pmid:30452352

    CrossRefPubMed