1,073
Views
0
CrossRef citations to date
0
Altmetric
Review Article

Ultrasound-based 3D bone modelling in computer assisted orthopedic surgery – a review and future challenges

, &

References

  • Baraza N, Chapman C, Zakani S, et al. 3D – Printed patient specific instrumentation in corrective osteotomy of the femur and pelvis: a review of the literature. 3D Print Med. 2020;6(1):34. doi: 10.1186/s41205-020-00087-0.
  • Evrard R, Schubert T, Paul L, et al. Quality of resection margin with patient specific instrument for bone tumor resection. J Bone Oncol. 2022;34:100434. doi: 10.1016/j.jbo.2022.100434.
  • Wong KC. CAOS in bone tumor surgery. In: Sugano N., editor, Computer assisted orthopaedic surgery for hip and knee: current state of the art in clinical application and basic research. Singapore: Springer; 2018. pp. 157–169.
  • Radermacher K, Portheine F, Anton M, et al. Computer assisted orthopaedic surgery with image based individual templates. Clin Orthop Relat Res. 1998;354(354):28–38. doi: 10.1097/00003086-199809000-00005.
  • Renson L, Poilvache P, van den Wyngaert H. Improved alignment and operating room efficiency with patient-specific instrumentation for TKA. Knee. 2014;21(6):1216–1220. doi: 10.1016/j.knee.2014.09.008.
  • Meng M, Wang J, Sun T, et al. Clinical applications and prospects of 3D printing guide templates in orthopaedics. J Orthop Translat. 2022;34:22–41. doi: 10.1016/j.jot.2022.03.001.
  • Asseln M, Grothues SAGA, Radermacher K. Relationship between the form and function of implant design in total knee replacement. J Biomech. 2021;119:110296. doi: 10.1016/j.jbiomech.2021.110296.
  • Culler SD, Martin GM, Swearingen A. Comparison of adverse events rates and hospital cost between customized individually made implants and standard off-the-shelf implants for total knee arthroplasty. Arthroplast Today. 2017;3(4):257–263. doi: 10.1016/j.artd.2017.05.001.
  • Brinkmann EJ, Fitz W. Custom total knee: understanding the indication and process. Arch Orthop Trauma Surg. 2021;141(12):2205–2216. doi: 10.1007/s00402-021-04172-9.
  • Pietrzak JR, Rowan FE, Kayani B, et al. Preoperative CT-based three-dimensional templating in Robot-assisted total knee arthroplasty more accurately predicts implant sizes than two-dimensional templating. J Knee Surg. 2019;32(7):642–648. doi: 10.1055/s-0038-1666829.
  • Twiggs JG, Wakelin EA, Roe JP, et al. Patient-specific simulated dynamics after total knee arthroplasty correlate with patient-reported outcomes. J Arthroplasty. 2018;33(9):2843–2850. doi: 10.1016/j.arth.2018.04.035.
  • Fischer MCM, Tokunaga K, Okamoto M, et al. Implications of the uncertainty of postoperative functional parameters for the preoperative planning of total hip arthroplasty. Journal Orthopaedic Research. 2022;40(11):2656–2662. doi: 10.1002/jor.25291.
  • Habor J, Fischer MCM, Tokunaga K, et al. The patient-specific combined target zone for morpho-functional planning of total hip arthroplasty. J Pers Med. 2021;11(8):817. doi: 10.3390/jpm11080817.
  • Reimann P, Brucker M, Arbab D, et al. Patient satisfaction – a comparison between patient-specific implants and conventional total knee arthroplasty. J Orthop. 2019;16(3):273–277. doi: 10.1016/j.jor.2019.03.020.
  • Chung BJ, Kang JY, Kang YG, et al. Clinical implications of femoral anthropometrical features for total knee arthroplasty in Koreans. J Arthroplasty. 2015;30(7):1220–1227. doi: 10.1016/j.arth.2015.02.014.
  • Mahoney OM, Kinsey T. Overhang of the femoral component in total knee arthroplasty: risk factors and clinical consequences. J Bone Joint Surg Am. 2010;92(5):1115–1121. doi: 10.2106/JBJS.H.00434.
  • Taubmann O, Berger M, Bögel M, et al. Medical imaging systems: an introductory guide: computed tomography. Cham (CH): Springer; 2018.
  • Centers for Medicare and Medicaid Services. Physician Fee Schedule. Available from: https://www.cms.gov/medicare/physician-fee-schedule/search?Y=0&T=4&HT=1&CT=0&H1=74176&H2=72195&H3=76856&M=5.
  • Von Haxthausen F, Hagenah J, Kaschwich M, et al. Robotized ultrasound imaging of the peripheral arteries – A phantom study. Current Directions in Biomedical Engineering. 2020;6(1)Sep 2020.10.1515/cdbme-2020-0033
  • Kim DM, Seo J-S, Jeon I-H, et al. Detection of rotator cuff tears by ultrasound: How many scans do novices need to be competent? Clin Orthop Surg. 2021;13(4):513–519. doi: 10.4055/cios20259.
  • Pearlman PC, Tagare HD, Sinusas AJ, et al. 3D radio frequency ultrasound cardiac segmentation using a linear predictor. Med Image Comput Comput Assist Interv. 2010;13(Pt 1):502–509. 10.1007/978-3-642-15705-9_6120879268
  • Huang Q, Zeng Z. A review on real-time 3D ultrasound imaging technology. Biomed Res Int. 2017;2017:6027029–6027020. doi: 10.1155/2017/6027029.
  • Che C, Mathai TS, Galeotti J. Ultrasound registration: a review. Methods. 2017;115:128–143. doi: 10.1016/j.ymeth.2016.12.006.
  • Pandey PU, Quader N, Guy P, et al. Ultrasound bone segmentation: a scoping review of techniques and validation practices. Ultrasound Med Biol. 2020;46(4):921–935. doi: 10.1016/j.ultrasmedbio.2019.12.014.
  • Heimann T, Meinzer H-P. Statistical shape models for 3D medical image segmentation: a review. Med Image Anal. 2009;13(4):543–563. doi: 10.1016/j.media.2009.05.004.
  • Hacihaliloglu I. 3D ultrasound for orthopedic interventions. Adv Exp Med Biol. 2018;1093:113–129. doi: 10.1007/978-981-13-1396-7_10.
  • Hacihaliloglu I. Ultrasound imaging and segmentation of bone surfaces: a review. Technology (Singap World Sci). 2017;5(2):74–80. doi: 10.1142/S2339547817300049.
  • Meiburger KM, Acharya UR, Molinari F. Automated localization and segmentation techniques for B-mode ultrasound images: a review. Comput Biol Med. 2018;92:210–235. doi: 10.1016/j.compbiomed.2017.11.018.
  • Shin Y, Yang J, Lee YH, et al. Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography. 2021;40(1):30–44. doi: 10.14366/usg.20080.
  • Morooka K, Nakamoto M, Sato Y. A survey on statistical modeling and machine learning approaches to computer assisted medical intervention: intraoperative anatomy modeling and optimization of interventional procedures. IEICE Trans Inf Syst. 2013;E96.D(4):784–797. doi: 10.1587/transinf.E96.D.784.
  • Sarkalkan N, Weinans H, Zadpoor AA. Statistical shape and appearance models of bones. Bone. 2014;60:129–140. doi: 10.1016/j.bone.2013.12.006.
  • Pandey P, Guy P, Hodgson AJ, et al. Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study. Int J Comput Assist Radiol Surg. 2018;13(10):1515–1524. doi: 10.1007/s11548-018-1788-5.
  • Kumar Jain A, Taylor RH. Understanding bone responses in B-mode ultrasound images and automatic bone surface extraction using a Bayesian probabilistic framework. SPIE; 2004. pp. 131–142.
  • Hacihaliloglu I, Abugharbieh R, Hodgson AJ, et al. 2A-4 enhancement of bone surface visualization from 3D ultrasound based on local phase information. IEEE Ultrasonics Symposium, 2006: 2–6 October 2006, [Vancouver, Canada]. Piscataway (NJ): IEEE Operations Center; 2006. pp. 21–24.
  • Quader N, Hodgson AJ, Mulpuri K, et al. Automatic evaluation of scan adequacy and dysplasia metrics in 2-D ultrasound images of the neonatal hip. Ultrasound Med Biol. 2017;43(6):1252–1262. doi: 10.1016/j.ultrasmedbio.2017.01.012.
  • Quader N, Hodgson AJ, Mulpuri K, et al. 3-D ultrasound imaging reliability of measuring dysplasia metrics in infants. Ultrasound Med Biol. 2021;47(1):139–153. doi: 10.1016/j.ultrasmedbio.2020.08.008.
  • Desai P, Hacihaliloglu I. Knee-Cartilage segmentation and thickness measurement from 2D ultrasound. J Imaging. 2019;5(4):43. doi: 10.3390/jimaging5040043.
  • Zhou G-Q, Li D-S, Zhou P, et al. Automating spine curvature measurement in volumetric ultrasound via adaptive phase features. Ultrasound Med Biol. 2020;46(3):828–841. doi: 10.1016/j.ultrasmedbio.2019.11.012.
  • Tu SJ, Morel J, Chen M, et al. Fast automatic bone surface segmentation in ultrasound images without machine learning. In Papiez BW, Yaqub M, Jiao J, Noble JA, Namburete AIL, editors. Medical Image Understanding and Analysis: 25th Annual Conference, MIUA 2021, Oxford, United Kingdom, July 12–14, 2021, proceedings. Cham: Springer; 2021. pp. 250–264.
  • Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJ, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. Red Hook: Curran Associates, Inc.; 2012.
  • Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. In Hornegger J, editor. Medical image computing and computer-assisted intervention – MICCAI 2015: 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III. Cham: Springer International Publishing AG; 2015. pp. 234–241.
  • Antico M, Sasazawa F, Dunnhofer M, et al. Deep learning-based femoral cartilage automatic segmentation in ultrasound imaging for guidance in robotic knee arthroscopy. Ultrasound Med Biol. 2020;46(2):422–435. doi: 10.1016/j.ultrasmedbio.2019.10.015.
  • Zaman A, Park SH, Miguel L, et al. Real-time 3D ultrasound bone model reconstruction and its registration with MR bone model for localization of intramedullary cystic Bone Lesion; 2019.
  • Duong DQ, Nguyen K-CT, Kaipatur NR, et al. Fully automated segmentation of alveolar bone using deep convolutional neural networks from intraoral ultrasound images. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference; 2019. pp. 6632–6635. doi: 10.1109/EMBC.2019.8857060.
  • El-Hariri H, Mulpuri K, Hodgson A, et al. Comparative evaluation of Hand-Engineered and Deep-Learned features for neonatal hip bone segmentation in ultrasound. In Shen D, editor, Medical image computing and computer assisted intervention – MICCAI 2019: 22nd international conference, Shenzhen, China, October 13–17, 2019, proceedings, Cham: Springer International Publishing; 2019. pp. 12–20.
  • Ungi T, Greer H, Sunderland KR, et al. Automatic spine ultrasound segmentation for scoliosis visualization and measurement. IEEE Trans Biomed Eng. 2020;67(11):3234–3241. doi: 10.1109/TBME.2020.2980540.
  • Banerjee S, Ling SH, Lyu J, et al. Automatic segmentation of 3D ultrasound spine curvature using convolutional neural Network Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference; 2020. p. 2039–2042.
  • Huang Z, Wang L-W, Leung FHF, et al. Bone feature segmentation in ultrasound spine image with robustness to speckle and regular occlusion noise. 2020 IEEE International Conference on Systems, Man, and Cybernetics: Toronto, Canada, October 11-14, 2020. Piscataway (NJ): IEEE; 2020 pp. 1566–1571.
  • Alsinan AZ, Rule C, Vives M, et al. GAN-Based realistic bone ultrasound image and label synthesis for improved segmentation. Cham: Springer; 2020. pp. 795–804.
  • Nguyen KCT, Duong DQ, Almeida FT, et al. Alveolar bone segmentation in intraoral ultrasonographs with machine learning. J Dent Res. 2020;99(9):1054–1061. doi: 10.1177/0022034520920593.
  • Zaman A, Park SH, Bang H, et al. Generative approach for data augmentation for deep learning-based bone surface segmentation from ultrasound images. Int J Comput Assist Radiol Surg. 2020;15(6):931–941. doi: 10.1007/s11548-020-02192-1.
  • Li R, Davoodi A, Cai Y, et al. Robot-assisted ultrasound reconstruction for spine surgery: from bench-top to pre-clinical study. Int J Comput Assist Radiol Surg. 2023;18(9):1613–1623. doi: 10.1007/s11548-023-02932-z.
  • Pan Y-C, Chan H-L, Kong X, et al. Multi-class deep learning segmentation and automated measurements in periodontal sonograms of a porcine model. Dento Maxillo Facial Radiol. 2022;51(3):20210363.
  • Kannan A, Hodgson A, Mulpuri K, et al. Uncertainty estimation for assessment of 3D US scan adequacy and DDH metric reliability. In Sudre CH, editor. Uncertainty for safe utilization of machine learning in medical imaging, and graphs in biomedical image analysis: second international workshop, UNSURE 2020, and third international workshop, GRAIL 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 8, 2020 proceedings. Cham: Springer; 2020. p. 97–105.
  • Kannan A, Hodgson A, Mulpuri K, et al. Leveraging voxel-wise segmentation uncertainty to improve reliability in assessment of paediatric dysplasia of the hip. Int J Comput Assist Radiol Surg. 2021;16(7):1121–1129. doi: 10.1007/s11548-021-02389-y.
  • Tang S, Yang X, Shajudeen P, et al. A CNN-based method to reconstruct 3-D spine surfaces from US images in vivo. Med Image Anal. 2021;74:102221. doi: 10.1016/j.media.2021.102221.
  • Hohlmann B, Brößner P, Radermacher K.CNN based 2D vs. 3D segmentation of bone in ultrasound images. EasyChair; 2022. pp. 116–110.
  • Du Toit C, Orlando N, Papernick S, et al. Automatic femoral articular cartilage segmentation using deep learning in three-dimensional ultrasound images of the knee. Osteoarthr Cartil Open. 2022;4(3):100290. doi: 10.1016/j.ocarto.2022.100290.
  • Antico M, Sasazawa F, Takeda Y, et al. Bayesian CNN for segmentation uncertainty inference on 4D ultrasound images of the femoral cartilage for guidance in robotic knee arthroscopy. IEEE Access. 2020;8:223961–223975. doi: 10.1109/ACCESS.2020.3044355.
  • Pandey PU, Guy P, Hodgson AJ. Can uncertainty estimation predict segmentation performance in ultrasound bone imaging? Int J CARS. 2022;17(5):825–832. doi: 10.1007/s11548-022-02597-0.
  • Wang P, Vives M, Patel VM, et al. Robust real-time bone surfaces segmentation from ultrasound using a local phase tensor-guided CNN. Int J Comput Assist Radiol Surg. 2020;15(7):1127–1135. doi: 10.1007/s11548-020-02184-1.
  • Alsinan AZ, Patel VM, Hacihaliloglu I. Automatic segmentation of bone surfaces from ultrasound using a filter-layer-guided CNN. Int J Comput Assist Radiol Surg. 2019;14(5):775–783. doi: 10.1007/s11548-019-01934-0.
  • Jiang B, Xu K, Moghekar A, et al. Feature-aggregated spatiotemporal spine surface estimation for wearable patch ultrasound volumetric imaging. In Medical imaging 2023: ultrasonic imaging and tomography: 22–23 February 2023, San Diego, California, United States. Bellingham, Washington, USA: SPIE; 2023. p. 23. doi: 10.1117/12.2653114.
  • Ghelich Oghli M, Shabanzadeh A, Moradi S, et al. Automatic fetal biometry prediction using a novel deep convolutional network architecture. Phys Med. 2021;88:127–137. doi: 10.1016/j.ejmp.2021.06.020.
  • Broessner P, Hohlmann B, Radermacher K. Ultrasound-based navigation of scaphoid fracture surgery. In Maier-Hein KH, Deserno TM, Handels H, Maier A, Palm C, Tolxdorff T, editors. Bildverarbeitung für die Medizin 2022: proceedings, german workshop on medical image computing, Heidelberg, June 26–28, 2022. Wiesbaden, Heidelberg: Springer Vieweg; 2022. p. 28–33.
  • Hohlmann B, Brößner P, Welle K, et al. Segmentation of the scaphoid bone in ultrasound images. Curr Dir Biomed Eng. 2021;7(1):76–80. doi: 10.1515/cdbme-2021-1017.
  • Hohlmann B, Glanz J, Radermacher K. Segmentation of the distal femur in ultrasound images. Curr Dir Biomed Eng. 2020;6(1):34. doi: 10.1515/cdbme-2020-0034.
  • Brosner P, Hohlmann B, Welle K, et al. Ultrasound-based registration for the computer-assisted navigated percutaneous scaphoid fixation. IEEE Trans Ultrason Ferroelectr Freq Control. 2023;70(9):1064–1072. doi: 10.1109/TUFFC.2023.3291387.
  • Kompella G, Antico M, Sasazawa F, et al. Segmentation of femoral cartilage from knee ultrasound images using mask R-CNN. Annual international conference of the IEEE engineering in medicine and biology society. IEEE Engineering in Medicine and Biology Society. Annual International Conference; 2019. pp. 966–969. doi: 10.1109/EMBC.2019.8857645.
  • Hohlmann B, Radermacher K. Augmented active shape model search – towards 3D ultrasound-based bone surface reconstruction. EPiC Series in Health Sciences. EasyChair; 2020. pp. 117–121.
  • Jiang W, Mei F, Xie Q. Novel automated spinal ultrasound segmentation approach for scoliosis visualization. Front Physiol. 2022;13:1051808. doi: 10.3389/fphys.2022.1051808.
  • Banerjee S, Lyu J, Huang Z, et al. Light-Convolution dense selection U-Net (LDS U-Net) for ultrasound lateral bony feature segmentation. Applied Sciences. 2021;11(21):10180. doi: 10.3390/app112110180.
  • Luan K, Li Z, Li J. An efficient end-to-end CNN for segmentation of bone surfaces from ultrasound. Comput Med Imaging Graph. 2020;84:101766. doi: 10.1016/j.compmedimag.2020.101766.
  • Dunnhofer M, Antico M, Sasazawa F, et al. Siam-U-Net: encoder-decoder siamese network for knee cartilage tracking in ultrasound images. Med Image Anal. 2020;60:101631. doi: 10.1016/j.media.2019.101631.
  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Advances in Neural Information Processing Systems; 2014. p. 27.
  • Mirza M, Osindero S. 2014. Conditional generative adversarial nets. arXiv.
  • Isola P, Zhu J-Y, Zhou T, et al. 2017 Image-to-Image translation with conditional adversarial networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE. doi: 10.1109/CVPR.2017.632.
  • Zhou Y, Rakkunedeth A, Keen C, et al. Wrist ultrasound segmentation by deep learning. In Michalowski M, Abidi SSR, Abidi S, editors. Artificial intelligence in medicine: 20th international conference on artificial intelligence in medicine, AIME 2022, Halifax, NS, Canada, June 14–17, 2022, Proceedings, 1st ed.. Cham: Springer International Publishing; Imprint Springer; 2022. pp. 230–237.
  • Alsinan AZ, Patel VM, Hacihaliloglu I. Bone shadow segmentation from ultrasound data for orthopedic surgery using GAN. Int J Comput Assist Radiol Surg. 2020;15(9):1477–1485. doi: 10.1007/s11548-020-02221-z.
  • Rahman A, Bandara WGC, Valanarasu JMJ, et al. Orientation-guided graph convolutional network for bone surface segmentation; 2022.
  • Brößner P, Hohlmann B, Radermacher K. Transformer vs. CNN – a comparison on Knee segmentation in ultrasound images. EasyChair; 2022. p. 31–24.
  • Mahfouz MR, Abdel Fatah EE, Johnson JM, et al. A novel approach to 3D bone creation in minutes 3D ultrasound. Bone Joint J. 2021;103-B(6 Supple A):81–86. doi: 10.1302/0301-620X.103B6.BJJ-2020-2455.R1.
  • Schumann S, Nolte L-P, Zheng G. Compensation of sound speed deviations in 3-D B-mode ultrasound for intraoperative determination of the anterior pelvic plane. IEEE Trans Inf Technol Biomed. 2012;16(1):88–97. doi: 10.1109/TITB.2011.2170844.
  • Hohlmann B, Broessner P, Phlippen L, et al. Knee bone models from ultrasound. IEEE Trans Ultrason Ferroelectr Freq Control. 2023;70(9):1054–1063. doi: 10.1109/TUFFC.2023.3286287.
  • Hänisch C, Hsu J, Noorman E, et al. Model based reconstruction of the bony knee anatomy from 3D ultrasound images. [15th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, 17.06.2015-20.06.2015, Vancouver, Canada], 5 Seiten; 2015.
  • Hohlmann B, Radermacher K. The interleaved partial active shape model (IPASM) search algorithm – towards 3D ultrasound-based bone surface reconstruction. EPiC Series in Health Sciences. EasyChair; 2019. pp. 177–180.
  • Myronenko A, Song X, Carreira-Perpiñán M. Non-rigid point set registration: coherent point drift. In: Schölkopf B, Platt J, Hoffman T, editors. Advances in neural information processing systems. Cambridge, MA: MIT Press; 2006.
  • Hacihaliloghlu I, Rasoulian A, Rohling RN, et al. Statistical shape model to 3D ultrasound registration for spine interventions using enhanced local phase features. Medical image computing and computer-assisted intervention MICCAI … International Conference on Medical Image Computing and Computer-Assisted Intervention, 16(Pt 2); 2013. pp. 361–368.
  • Hacihaliloglu I, Rasoulian A, Rohling RN, et al. Local phase tensor features for 3-D ultrasound to statistical shape + pose spine model registration. IEEE Trans Med Imaging. 2014;33(11):2167–2179. doi: 10.1109/TMI.2014.2332571.
  • Rasoulian A, Rohling RN, Abolmaesumi P. 2013, Augmentation of paramedian 3D ultrasound images of the spine. In Barratt D, Cotin S, Fichtinger G, Jannin P, Navab N, editors. Information processing in computer-assisted interventions: 4th international conference, IPCAI 2013, Heidelberg, Germany, June 26, 2013. Proceedings. Berlin, Heidelberg: Springer; pp. 51–60.
  • Rasoulian A, Rohling RN, Abolmaesumi P. Probabilistic registration of an unbiased statistical shape model to ultrasound images of the spine. In Medical imaging 2012: image-guided procedures, robotic interventions, and modeling. SPIE; 2012. p. 83161P. doi: 10.1117/12.911742.
  • Ghanavati S, Mousavi P, Fichtinger G, et al. Phantom validation for ultrasound to statistical shape model registration of human pelvis. Medical imaging 2011: visualization, image-guided procedures, and modeling. SPIE; 2011. p.79642U. doi: 10.1117/12.876998.
  • Ghanavati S, Mousavi P, Fichtinger G, et al. Multi-slice to volume registration of ultrasound data to a statistical atlas of human pelvis. Medical imaging 2010: visualization, image-guided procedures, and modeling. SPIE; 2010. p. 76250O. doi: 10.1117/12.844080.
  • Khallaghi S, Abolmaesumi P, Gong RH, et al. GPU accelerated registration of a statistical shape model of the lumbar spine to 3D ultrasound images. In Medical imaging 2011: visualization, image-guided procedures, and modeling. SPIE; 2011. p. 79642W. doi: 10.1117/12.878377.
  • Jiang W-W, Zhong X-X, Zhou G-Q, et al. An automatic measurement method of spinal curvature on ultrasound coronal images in adolescent idiopathic scoliosis. Math Biosci Eng. 2019;17(1):776–788. doi: 10.3934/mbe.2020040.
  • Cengizler C, Kerem Ün M, Buyukkurt S. A novel evolutionary method for spine detection in ultrasound samples of spina bifida cases. Comput Methods Programs Biomed. 2021;198:105787. doi: 10.1016/j.cmpb.2020.105787.
  • Huang Z, Zhao R, Leung FHF, et al. Joint spine segmentation and noise removal From ultrasound volume projection images With selective feature sharing. IEEE Trans Med Imaging. 2022;41(7):1610–1624. doi: 10.1109/TMI.2022.3143953.
  • Rahman A, Valanarasu JMJ, Hacihaliloglu I, et al. Simultaneous bone and shadow segmentation network using task correspondence consistency. In Wang L, Dou Q, Fletcher PT, Speidel S, Li S, editors. Medical image computing and computer assisted intervention – MICCAI 2022. Switzerland, Cham: Springer Nature; 2022. pp. 330–339.
  • Hänisch C, Hohlmann B, Radermacher K. The interleaved partial active shape model search (IPASM) algorithm–Preliminary results of a novel approach towards 3D ultrasound-based bone surface reconstruction. In EPiC Series in Health Sciences. EasyChair; 2017. pp. 399–406.
  • Pandey P, Hohlmann B, Brößner P, et al. Standardized evaluation of current ultrasound bone segmentation algorithms on multiple datasets. EasyChair; 2022. p. 148–141.
  • Zhou B, Zhao H, Puig X, et al. Scene parsing through ADE20K dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): Proceedings 21–26 July 2016, Honolulu, Hawaii. Piscataway (NJ): IEEE. 2017. pp. 5122–5130. doi: 10.1109/CVPR.2017.544.
  • Tchapmi LP, Kosaraju V, Rezatofighi H, et al. TopNet: structural point cloud decoder. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE; 2019. doi: 10.1109/CVPR.2019.00047.
  • Xie H, Yao H, Zhou S, et al. GRNet: gridding residual network for dense point cloud completion. ECCV; 2020.