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. 2019 Dec;26(12):1695-1706.
doi: 10.1016/j.acra.2019.07.006. Epub 2019 Aug 10.

Automated Segmentation of Tissues Using CT and MRI: A Systematic Review

Leon Lenchik  1 , Laura Heacock  2 , Ashley A Weaver  3 , Robert D Boutin  4 , Tessa S Cook  5 , Jason Itri  6 , Christopher G Filippi  7 , Rao P Gullapalli  8 , James Lee  9 , Marianna Zagurovskaya  9 , Tara Retson  10 , Kendra Godwin  11 , Joey Nicholson  12 , Ponnada A Narayana  13
Affiliations

Automated Segmentation of Tissues Using CT and MRI: A Systematic Review

Leon Lenchik et al. Acad Radiol. 2019 Dec.

Abstract

Rationale and objectives: The automated segmentation of organs and tissues throughout the body using computed tomography and magnetic resonance imaging has been rapidly increasing. Research into many medical conditions has benefited greatly from these approaches by allowing the development of more rapid and reproducible quantitative imaging markers. These markers have been used to help diagnose disease, determine prognosis, select patients for therapy, and follow responses to therapy. Because some of these tools are now transitioning from research environments to clinical practice, it is important for radiologists to become familiar with various methods used for automated segmentation.

Materials and methods: The Radiology Research Alliance of the Association of University Radiologists convened an Automated Segmentation Task Force to conduct a systematic review of the peer-reviewed literature on this topic.

Results: The systematic review presented here includes 408 studies and discusses various approaches to automated segmentation using computed tomography and magnetic resonance imaging for neurologic, thoracic, abdominal, musculoskeletal, and breast imaging applications.

Conclusion: These insights should help prepare radiologists to better evaluate automated segmentation tools and apply them not only to research, but eventually to clinical practice.

Keywords: CT; MRI; Machine learning; Quantitative imaging; Segmentation.

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Figures

Figure 1:
Figure 1:
Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram showing identification, screening, eligibility, and inclusion of articles.
See this image and copyright information in PMC

References

Appendix B: Manuscripts Included in Systematic Review

B1. Neurosegmentation
    1. Abbasi S, Tajeripour F. Detection of brain tumor in 3D MRI images using local binary patterns and histogram orientation gradient. Neurocomputing. 2017; 219:526–535.
    1. Adamson C, Da Costa AC, Beare R, Wood AG. Automatic intracranial space segmentation for computed tomography brain images. J. Digit. Imaging. 2013; 26(3):563–571. - PMC - PubMed
    1. Adamson C, Beare R, Walterfang M, Seal M. Software Pipeline for Midsagittal Corpus Callosum Thickness Profile Processing: Automated Segmentation, Manual Editor, Thickness Profile Generator, Group-Wise Statistical Comparison and Results Display. Neuroinformatics. 2014; 12(4):595–614. - PubMed
    1. Adler S, Wagstyl K, Gunny R, et al. Novel surface features for automated detection of focal cortical dysplasias in paediatric epilepsy. NeuroImage Clin. 2017; 14:18–27. - PMC - PubMed
    1. Aghdasi N, Li Y, Berens A, et al. Efficient orbital structures segmentation with prior anatomical knowledge. J. Med. Imaging 2017; 4(3): 034501. - PMC - PubMed

B2. Thoracic Segmentation

    1. Abbas Q Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases. Biomed Signal Process Control. 2017;33:325–34.
    1. Albà X, Figueras I Ventura RM, Lekadir K, Tobon-Gomez C, Hoogendoorn C, Frangi AF. Automatic cardiac LV Segmentation in MRI using modified graph cuts with smoothness and interslice constraints. Magn Reson Med. 2014;72(6):1775–84. - PubMed
    1. Albà X, Lekadir K, Pereañez M, Medrano-Gracia P, Young AA, Frangi AF. Automatic initialization and quality control of large-scale cardiac MRI segmentations. Med Image Anal. 2018;43:129–41. - PubMed
    1. Anders K, Achenbach S, Petit I, Daniel WG, Uder M, Pflederer T. Accuracy of automated software-guided detection of significant coronary artery stenosis by CT angiography: Comparison with invasive catheterisation. Eur Radiol. 2013;23(5):1218–25. - PubMed
    1. Anthimopoulos M, Christodoulidis S, Ebner L, Christe A, Mougiakakou S. Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network. IEEE Trans Med Imaging. 2016;35(5):1207–16. - PubMed

B3. Abdominal Segmentation

    1. Acosta O, Mylona E, Le Dain M, Voisin C, Lizee T, Rigaud B, et al. Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy. Radiother Oncol. 2017;125(3):492–9. - PubMed
    1. Akhondi-Asl A, Hoyte L, Lockhart ME, Warfield SK. A logarithmic opinion pool based STAPLE algorithm for the fusion of segmentations with associated reliability weights. IEEE Trans Med Imaging. 2014;33(10):1997–2009. - PMC - PubMed
    1. Anter AMH AE; ElSoud MA; Azar AT Automatic liver parenchyma segmentation system from abdominal CT scans using hybrid techniques. International Journal of Biomedical Engineering and Technology.01 January 2015;17(2):148–167.
    1. Cai J, Lu L, Zhang Z, Xing F, Yang L, Yin Q. Pancreas Segmentation in MRI using Graph-Based Decision Fusion on Convolutional Neural Networks. Med Image Comput Comput Assist Interv. 2016;9901:442–50. - PMC - PubMed
    1. Cai W, He B, Fan Y, Fang C, Jia F. Comparison of liver volumetry on contrast-enhanced CT images: one semiautomatic and two automatic approaches. J Appl Clin Med Phys. 2016;17(6):118–27. - PMC - PubMed

B4. Musculoskeletal Segmentation

    1. Ahn C, Bui TD, Lee YW, Shin J, Park H. Fully automated, level set-based segmentation for knee MRIs using an adaptive force function and template: data from the osteoarthritis initiative. Biomed Eng Online. 2016. August 24;15(1):99. doi: 10.1186/s12938-016-0225-7. - DOI - PMC - PubMed
    1. Almeida DF, Ruben RB, Folgado J, Fernandes PR, Audenaert E, Verhegghe B, et al. Fully automatic segmentation of femurs with medullary canal definition in high and in low resolution CT scans. Med Eng Phys. 2016;38(12):1474–80. - PubMed
    1. Anas EM, Rasoulian A, Seitel A, Darras K, Wilson D, John PS, et al. Automatic Segmentation of Wrist Bones in CT Using a Statistical Wrist Shape + Pose Model. IEEE Trans Med Imaging. 2016;35(8):1789–801. - PubMed
    1. Athertya JS, Saravana Kumar G. Automatic segmentation of vertebral contours from CT images using fuzzy corners. Comput Biol Med. 2016;72:75–89. - PubMed
    1. Bowes MA, Vincent GR, Wolstenholme CB, Conaghan PG. A novel method for bone area measurement provides new insights into osteoarthritis and its progression. Ann Rheum Dis. 2015. March;74(3):519–25. doi: 10.1136/annrheumdis-2013-204052. - DOI - PubMed

B5. Breast Segmentation

    1. Agner SC, Xu J, Madabhushi A. Spectral embedding based active contour (SEAC) for lesion segmentation on breast dynamic contrast enhanced magnetic resonance imaging. Medical physics. 2013. March 1;40(3). - PMC - PubMed
    1. Al-Faris AQ, Ngah UK, Isa NA, Shuaib IL. Computer-aided segmentation system for breast MRI tumour using modified automatic seeded region growing (BMRI-MASRG). Journal of digital imaging. 2014. February 1;27(1):133–44. - PMC - PubMed
    1. DalmƖş MU, Litjens G, Holland K, Setio A, Mann R, Karssemeijer N, Gubern Mérida A. Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Medical physics. 2017. February 1;44(2):533–46. - PubMed
    1. DalmƖş MU, Vreemann S, Kooi T, Mann RM, Karssemeijer N, Gubern-Mérida A. Fully automated detection of breast cancer in screening MRI using convolutional neural networks. Journal of Medical Imaging. 2018. January;5(1):014502. - PMC - PubMed
    1. Doran SJ, Hipwell JH, Denholm R, Eiben B, Busana M, Hawkes DJ, Leach MO, Silva ID. Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?. Medical physics. 2017. September;44(9):4573–92. - PMC - PubMed

B6. Adipose Tissue Segmentation

    1. Addeman BT, Kutty S, Perkins TG, Soliman AS, Wiens CN, McCurdy CM, Beaton MD, Hegele RA, McKenzie CA. Validation of volumetric and single slice MRI adipose analysis using a novel fully automated segmentation method. Journal of Magnetic Resonance Imaging. 2015. January;41(1):233–41. - PubMed
    1. Commandeur F, Goeller M, Betancur J, Cadet S, Doris M, Chen X, Berman DS, Slomka PJ, Tamarappoo BK, Dey D. Deep learning for quantification of epicardial and thoracic adipose tissue from non-contrast CT. IEEE transactions on medical imaging. 2018. August;37(8):1835–46. - PMC - PubMed
    1. Decazes P, Rouquette A, Chetrit A, Vera P, Gardin I. Automatic measurement of the total visceral adipose tissue from computed tomography images by using a multi-atlas segmentation method. Journal of computer assisted tomography. 2018. January 1;42(1):139–45. - PubMed
    1. Ding X, Terzopoulos D, Diaz Zamudio M, Berman DS, Slomka PJ, Dey D. Automated pericardium delineation and epicardial fat volume quantification from noncontrast CT. Medical physics. 2015. September 1;42(9):5015–26. - PubMed
    1. Fallah F, Machann J, Martirosian P, Bamberg F, Schick F, Yang B. Comparison of T1-weighted 2D TSE, 3D SPGR, and two-point 3D Dixon MRI for automated segmentation of visceral adipose tissue at 3 Tesla. Magnetic Resonance Materials in Physics, Biology and Medicine. 2017. April 1;30(2):139–51. - PubMed

References

    1. McBee MP, Awan OA, Colucci AT, Ghobadi CW, Kadom N, Kansagra AP, Tridandapani S, Auffermann WF. Deep learning in radiology. Academic Radiology. 2018. November 1;25(11):1472–80. - PubMed
    1. Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Zeitschrift für Medizinische Physik. 2018. December 13. - PubMed
    1. Zhang Y, Chen JH, Chang KT, Park VY, Kim MJ, Chan S, Chang P, Chow D, Luk A, Kwong T, Su MY. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net. Academic Radiology. 2019. January 31. - PMC - PubMed
    1. Stanzione A, Cuocolo R, Cocozza S, Romeo V, Persico F, Fusco F, Longo N, Brunetti A, Imbriaco M. Detection of Extraprostatic Extension of Cancer on Biparametric MRI Combining Texture Analysis and Machine Learning: Preliminary Results. Academic Radiology. 2019. January 1. - PubMed
    1. England JR, Cheng PM. Artificial intelligence for medical image analysis: a guide for authors and reviewers. American Journal of Roentgenology. 2019. March;212(3):513–9. - PubMed

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