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

Leon Lenchik, Laura Heacock, Ashley A Weaver, Robert D Boutin, Tessa S Cook, Jason Itri, Christopher G Filippi, Rao P Gullapalli, James Lee, Marianna Zagurovskaya, Tara Retson, Kendra Godwin, Joey Nicholson, Ponnada A Narayana, Leon Lenchik, Laura Heacock, Ashley A Weaver, Robert D Boutin, Tessa S Cook, Jason Itri, Christopher G Filippi, Rao P Gullapalli, James Lee, Marianna Zagurovskaya, Tara Retson, Kendra Godwin, Joey Nicholson, Ponnada A Narayana

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.

Copyright © 2019 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

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.

References

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    1. Zhao L, Jia K. Multiscale cnns for brain tumor segmentation and diagnosis. Comput. Math. Methods Med 2016; 2016.
    1. Zhu H, Cheng H, Yang X, et al. Metric learning for multi-atlas based segmentation of hippocampus. Neuroinformatics. 2017; 15(1):41–50.
    1. Zhuge Y, Krauze AV, Ning H, et al. Brain tumor segmentation using holistically nested neural networks in MRI images. Med. Phys. 2017; 44(10):5234–5243.
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.
    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.
    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.
    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.
    1. Bragman FJS, McClelland JR, Jacob J, Hurst JR, Hawkes DJ. Pulmonary Lobe Segmentation with Probabilistic Segmentation of the Fissures and a Groupwise Fissure Prior. IEEE Trans Med Imaging. 2017;36(8):1650–63.
    1. Bustamante M, Petersson S, Eriksson J, Alehagen U, Dyverfeldt P, Carlhäll CJ, et al. Atlas-based analysis of 4D flow CMR: Automated vessel segmentation and flow quantification. J Cardiovasc Magn Reson. 2015;17:87.
    1. Cai Y, Islam A, Bhaduri M, Chan I, Li S. Unsupervised Freeview Groupwise Cardiac Segmentation Using Synchronized Spectral Network. IEEE Trans Med Imaging. 2016;35(9):2174–88.
    1. Cao Q, Broersen A, de Graaf MA, Kitslaar PH, Yang G, Scholte AJ, et al. Automatic identification of coronary tree anatomy in coronary computed tomography angiography. Int J Cardiovasc Imaging. 2017;33(11):1809–19.
    1. Cochet H, Denis A, Komatsu Y, Jadidi AS, Aït Ali T, Sacher F, et al. Automated Quantification of Right Ventricular Fat at Contrast-enhanced Cardiac Multidetector CT in Arrhythmogenic Right Ventricular Cardiomyopathy. Radiology. 2015;275(3):683–91.
    1. Cruz-Aceves I, Avina-Cervantes JG, Lopez-Hernandez JM, Garcia-Hernandez MG, Ibarra-Manzano MA. Unsupervised Cardiac Image Segmentation via Multiswarm Active Contours with a Shape Prior. Comput Math Methods Med. 2013;2013:1–10.
    1. De Vos BD, Wolterink JM, De Jong PA, Leiner T, Viergever MA, Isgum I. ConvNet-Based Localization of Anatomical Structures in 3-D Medical Images. IEEE Trans Med Imaging. 2017;36(7):1470–81.
    1. Eilot D, Goldenberg R. Fully automatic model-based calcium segmentation and scoring in coronary CT angiography. Int J Comput Assist Radiol Surg. 2014;9(4):595–608.
    1. Elattar MA, Wiegerinck EM, Planken RN, Vanbavel E, Van Assen HC, Baan J, et al. Automatic segmentation of the aortic root in CT angiography of candidate patients for transcatheter aortic valve implantation. Med Biol Eng Comput. 2014;52(7):611–8.
    1. Eslami A, Karamalis A, Katouzian A, Navab N. Segmentation by retrieval with guided random walks: Application to left ventricle segmentation in MRI. Med Image Anal. 2013;17(2):236–53.
    1. Feng C, Zhang S, Zhao D, Li C. Simultaneous extraction of endocardial and epicardial contours of the left ventricle by distance regularized level sets. Med Phys. 2016;43(6):2741–55.
    1. Gao X, Boccalini S, Kitslaar PH, Budde RPJ, Attrach M, Tu S, et al. Quantification of aortic annulus in computed tomography angiography: Validation of a fully automatic methodology. Eur J Radiol. 2017;93:1–8.
    1. Gao X, Kitslaar PH, Budde RPJ, Tu S, de Graaf MA, Xu L, et al. Automatic detection of aortofemoral vessel trajectory from whole-body computed tomography angiography data sets. Int J Cardiovasc Imaging. 2016;32(8):1311–22.
    1. Gill G, Bauer C, Beichel RR. A method for avoiding overlap of left and right lungs in shape model guided segmentation of lungs in CT volumes. Med Phys. 2014;41(10):101908.
    1. Gill G, Beichel RR. An approach for reducing the error rate in automated lung segmentation. Comput Biol Med. 2016;76:143–53.
    1. Goel A, McColl R, King KS, Whittemore A, Peshock RM. Fully automated tool to identify the aorta and compute flow using phase-contrast MRI: Validation and application in a large population based study. J Magn Reson Imaging. 2014;40(1):221–8.
    1. Guo Y, Zhou C, Chan HP, Chughtai A, Wei J, Hadjiiski LM, et al. Automated iterative neutrosophic lung segmentation for image analysis in thoracic computed tomography. Med Phys. 2013;40(8):081912.
    1. Hajiaghayi M, Groves EM, Jafarkhani H, Kheradvar A. A 3-D active contour method for automated segmentation of the left ventricle from magnetic resonance images. IEEE Trans Biomed Eng. 2017;64(1):134–44.
    1. Han D, Shim H, Jeon B, Jang Y, Hong Y, Jung S, et al. Automatic coronary artery segmentation using active search for branches and seemingly disconnected vessel segments from coronary CT angiography. PLoS One. 2016;11(8):e0156837.
    1. Harmouche R, San Jose Estepar R, Ross JC. Automatic inspiratory and expiratory left and right lung segmentation for disease characterization [Internet]. Vol. 189, American Journal of Respiratory and Critical Care Medicine; 2014. p. A4354 Available from: .
    1. Hsu LY, Jacobs M, Benovoy M, Ta AD, Conn HM, Winkler S, et al. Diagnostic Performance of Fully Automated Pixel-Wise Quantitative Myocardial Perfusion Imaging by Cardiovascular Magnetic Resonance. JACC Cardiovasc Imaging. 2018;11(5):697–707.
    1. Hu H, Gao Z, Liu L, Liu H, Gao J, Xu S, et al. Automatic segmentation of the left ventricle in cardiac MRI using local binary fitting model and dynamic programming techniques. PLoS One. 2014;9(12):e114760.
    1. Kaderka R, Gillespie EF, Mundt RC, Bryant AK, Sanudo-Thomas CB, Harrison AL, et al. Geometric and dosimetric evaluation of atlas based auto-segmentation of cardiac structures in breast cancer patients. Radiotherapy and Oncology. 2019;215–20.
    1. Kang HC, Kim B, Lee J, Shin J, Shin YG. Automatic left and right heart segmentation using power watershed and active contour model without edge. Biomed Eng Lett. 2014;4(4):355–61.
    1. Kohlmann P, Strehlow J, Jobst B, Krass S, Kuhnigk JM, Anjorin A, et al. Automatic lung segmentation method for MRI-based lung perfusion studies of patients with chronic obstructive pulmonary disease. Int J Comput Assist Radiol Surg. 2015;10(4):403–17.
    1. Kumamaru KK, George E, Aghayev A, Saboo SS, Khandelwal A, Rodríguez-López S, et al. Implementation and performance of automated software for computing right-to-left ventricular diameter ratio from computed tomography pulmonary angiography images. J Comput Assist Tomogr. 2016;40(3):387–92.
    1. Kurugol S, Come CE, Diaz AA, Ross JC, Kinney GL, Black-Shinn JL, et al. Automated quantitative 3D analysis of aorta size, morphology, and mural calcification distributions. Med Phys. 2015;42(9):5467–78.
    1. Kurzendorfer T, Forman C, Schmidt M, Tillmanns C, Maier A, Brost A. Fully automatic segmentation of left ventricular anatomy in 3-D LGE-MRI. Comput Med Imaging Graph. 2017;59:13–27.
    1. Lassen B, Van Rikxoort EM, Schmidt M, Kerkstra S, Van Ginneken B, Kuhnigk JM. Automatic segmentation of the pulmonary lobes from chest CT scans based on Fissures, Vessels, and Bronchi. IEEE Trans Med Imaging. 2013;32(2):210–22.
    1. Lin K, Collins JD, Lloyd-Jones DM, Jolly MP, Li D, Markl M, et al. Automated Assessment of Left Ventricular Function and Mass Using Heart Deformation Analysis: Initial Experience in 160 Older Adults. Acad Radiol. 2016;23(3):321–5.
    1. Matsumoto AJ, Bartholmai BJ, Wylam ME. Comparison of Total Lung Capacity Determined by Plethysmography with Computed Tomographic Segmentation Using CALIPER. J Thorac Imaging. 2017;32(2):101–6.
    1. Medrano-Gracia P, Cowan BR, Ambale-Venkatesh B, Bluemke DA, Eng J, Finn JP, et al. Left ventricular shape variation in asymptomatic populations: The multi-ethnic study of atherosclerosis. J Cardiovasc Magn Reson. 2014;16:56.
    1. Meng Q, Kitasaka T, Nimura Y, Oda M, Ueno J, Mori K. Automatic segmentation of airway tree based on local intensity filter and machine learning technique in 3D chest CT volume. Int J Comput Assist Radiol Surg. 2017;12(2):245–61.
    1. Molaei S, Shiri ME, Horan K, Kahrobaei D, Nallamothu B, Najarian K. Deep Convolutional Neural Networks for left ventricle segmentation. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; 2017. p. 668–71.
    1. Moolan-Feroze O, Mirmehdi M, Hamilton M, Bucciarelli-Ducci C. Segmentation of the right ventricle using diffusion maps and Markov random fields. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. p. 682–9.
    1. Morais P, Queirós S, Heyde B, Engvall J, Hooge JD, Vilaça JL. Fully automatic left ventricular myocardial strain estimation in 2D short-axis tagged magnetic resonance imaging. Phys Med Biol. 2017;62(17):6899–919.
    1. Moses D, Sammut C, Zrimec T. Automatic segmentation and analysis of the main pulmonary artery on standard post-contrast CT studies using iterative erosion and dilation. Int J Comput Assist Radiol Surg. 2016;11(3):381–95.
    1. Nageswararao AV, Srinivasan S, Babu Peter S. Automatic hybrid ventricular segmentation of short-axis cardiac MRI images. Biomed Res. 2017;28(13):5816–24.
    1. Noor NM, Than JCM, Rijal OM, Kassim RM, Yunus A, Zeki AA, et al. Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm. J Med Syst. 2015;39(3):22.
    1. Queirós S, Barbosa D, Engvall J, Ebbers T, Nagel E, Sarvari SI, et al. Multi-centre validation of an automatic algorithm for fast 4D myocardial segmentation in cine CMR datasets. Eur Heart J Cardiovasc Imaging. 2016;17(10):1118–27.
    1. Rebouças Filho PP, Cortez PC, da Silva Barros AC, Victor VH, Tavares RSJM. Novel and powerful 3D adaptive crisp active contour method applied in the segmentation of CT lung images. Med Image Anal. 2017;35:503–16.
    1. Rengier F, Wörz S, Melzig C, Ley S, Fink C, Benjamin N, et al. Automated 3D volumetry of the pulmonary arteries based on magnetic resonance angiography has potential for predicting pulmonary hypertension. PLoS One. 2016;11(9):e0162516.
    1. Ringenberg J, Deo M, Devabhaktuni V, Berenfeld O, Boyers P, Gold J. Fast, accurate, and fully automatic segmentation of the right ventricle in short-axis cardiac MRI. Comput Med Imaging Graph. 2014;38(3):190–201.
    1. Rodrigues O, Rodrigues LO, Oliveira LSN, Conci A, Liatsis P. Automated recognition of the pericardium contour on processed CT images using genetic algorithms. Comput Biol Med. 2017;87:38–45.
    1. Ross JC, Kindlmann GL, Okajima Y, Hatabu H, Díaz AA, Silverman EK, et al. Pulmonary lobe segmentation based on ridge surface sampling and shape model fitting. Med Phys. 2013;40(12):121903.
    1. Shahzad R, Bos D, Budde RPJ, Pellikaan K, Niessen WJ, Van Der Lugt A, et al. Automatic segmentation and quantification of the cardiac structures from non-contrast-enhanced cardiac CT scans. Phys Med Biol. 2017;62(9):3798–813.
    1. Shahzad R, Tao Q, Dzyubachyk O, Staring M, Lelieveldt BPF, van der Geest RJ. Fully-automatic left ventricular segmentation from long-axis cardiac cine MR scans. Med Image Anal. 2017;39:44–55.
    1. Soliman A, Khalifa F, Elnakib A, El-Ghar MA, Dunlap N, Wang B, et al. Accurate lungs segmentation on CT chest images by adaptive appearance-guided shape modeling. IEEE Trans Med Imaging. 2017;36(1):263–76.
    1. Sugiura T, Takeguchi T, Sakata Y, Nitta S, Okazaki T, Matsumoto N, et al. Automatic model-based contour detection of left ventricle myocardium from cardiac CT images. Int J Comput Assist Radiol Surg. 2013;8(1):145–55.
    1. Sun K, Udupa JK, Odhner D, Tong Y, Zhao L, Torigian DA. Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration. Med Phys. 2016;43(3):1487–500.
    1. Takx RAP, De Jong PA, Leiner T, Oudkerk M, De Koning HJ, Mol CP, et al. Automated coronary artery calcification scoring in non-gated chest CT: Agreement and reliability. PLoS One. 2014;9(3):e91239.
    1. Tan LK, McLaughlin RA, Lim E, Abdul Aziz YF, Liew YM. Fully automated segmentation of the left ventricle in cine cardiac MRI using neural network regression. J Magn Reson Imaging. 2018;48(1):140–52.
    1. Tsadok Y, Petrank Y, Sarvari S, Edvardsen T, Adam D. Automatic segmentation of cardiac MRI cines validated for long axis views. Comput Med Imaging Graph. 2013;37(7–8):500–11.
    1. Tufvesson J, Hedström E, Steding-Ehrenborg K, Carlsson M, Arheden H, Heiberg E. Validation and Development of a New Automatic Algorithm for Time-Resolved Segmentation of the Left Ventricle in Magnetic Resonance Imaging. Biomed Res Int. 2015;2015:1–12.
    1. Wang B, Gu X, Fan C, Xie H, Zhang S, Tian X, et al. Sparse group composition for robust left ventricular epicardium segmentation. Comput Med Imaging Graph. 2015;46:56–63.
    1. Wang L, Pei M, Codella NCF, Kochar M, Weinsaft JW, Li J, et al. Left Ventricle: Fully Automated Segmentation Based on Spatiotemporal Continuity and Myocardium Information in Cine Cardiac Magnetic Resonance Imaging (LV-FAST). Biomed Res Int. 2015;2015:1–9.
    1. Wang Z, Bhatia KK, Glocker B, Marvao A, Dawes T, Misawa K, et al. Geodesic patch-based segmentation. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2014. p. 666–73.
    1. Wei D, Sun Y, Ong SH, Chai P, Teo LL, Low AF. A comprehensive 3-D framework for automatic quantification of late gadolinium enhanced cardiac magnetic resonance images. IEEE Trans Biomed Eng. 2013;60(6):1499–508.
    1. Wei Y, Shen G, Li JJ. A fully automatic method for lung parenchyma segmentation and repairing. J Digit Imaging. 2013;26(3):483–95.
    1. Wolterink JM, Leiner T, de Vos BD, van Hamersvelt RW, Viergever MA, Išgum I. Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks. Med Image Anal. 2016;34:123–36.
    1. Wolterink JM, Leiner T, Takx RAP, Viergever MA, Išgum I. Automatic Coronary Calcium Scoring in Non-Contrast-Enhanced ECG-Triggered Cardiac CT with Ambiguity Detection. IEEE Trans Med Imaging. 2015;34(9):1867–78.
    1. Xie Y, Padgett J, Biancardi AM, Reeves AP. Automated aorta segmentation in low-dose chest CT images. Int J Comput Assist Radiol Surg. 2014;9(2):211–9.
    1. Xu Z, Bagci U, Jonsson C, Jain S, Mollura DJ. Accurate and efficient separation of left and right lungs from 3D CT scans: A generic hysteresis approach. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC; 2014. 2014. p. 6036–9.
    1. Yang G, Chen Y, Ning X, Sun Q, Shu H, Coatrieux JL. Automatic coronary calcium scoring using noncontrast and contrast CT images. Med Phys. 2016;43(5):2174–86.
    1. Zhang W, Wang X, Zhang P, Chen J. Global optimal hybrid geometric active contour for automated lung segmentation on CT images. Comput Biol Med. 2017;91:168–80.
    1. Zhen X, Zhang H, Islam A, Bhaduri M, Chan I, Li S. Direct and simultaneous estimation of cardiac four chamber volumes by multioutput sparse regression. Med Image Anal. 2017;36:184–96.
    1. Zhou H, Sun P, Ha S, Lundine D, Xiong G. Watertight modeling and segmentation of bifurcated Coronary arteries for blood flow simulation using CT imaging. Comput Med Imaging Graph. 2016;53:43–53.
    1. Zhou J, Yan Z, Lasio G, Huang J, Zhang B, Sharma N, et al. Automated compromised right lung segmentation method using a robust atlas-based active volume model with sparse shape composition prior in CT. Comput Med Imaging Graph. 2015;46:47–55.
    1. Zhu L, Gao Y, Appia V, Yezzi A, Arepalli C, Faber T, et al. A complete system for automatic extraction of left ventricular myocardium from ct images using shape segmentation and contour evolution. IEEE Trans Image Process. 2014;23(3):1340–51.
    1. Zhu L, Gao Y, Appia V, Yezzi A, Arepalli C, Faber T, et al. Automatic delineation of the myocardial wall from CT images via shape segmentation and variational region growing. IEEE Trans Biomed Eng. 2013;60(10):2887–95.
    1. Zhu L, Gao Y, Yezzi A, Tannenbaum A. Automatic segmentation of the left atrium from MR images via variational region growing with a moments-based shape prior. IEEE Trans Image Process. 2013;22(12):5111–22.
    1. Zhuang X, Bai W, Song J, Zhan S, Qian X, Shi W, et al. Multiatlas whole heart segmentation of CT data using conditional entropy for atlas ranking and selection. Med Phys. 2015;42(7):3822–33.
    1. Zhuang X, Shen J. Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI. Med Image Anal. 2016;31:77–87.
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.
    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.
    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.
    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.
    1. Chandra SS, Dowling JA, Greer PB, Martin J, Wratten C, Pichler P, et al. Fast automated segmentation of multiple objects via spatially weighted shape learning. Phys Med Biol. 2016;61(22):8070–84.
    1. Cheng R, Roth HR, Lay N, Lu L, Turkbey B, Gandler W, et al. Automatic magnetic resonance prostate segmentation by deep learning with holistically nested networks. J Med Imaging (Bellingham). 2017;4(4):041302.
    1. Cheng R, Turkbey B, Gandler W, Agarwal HK, Shah VP, Bokinsky A, et al. Atlas based AAM and SVM model for fully automatic MRI prostate segmentation. Conf Proc IEEE Eng Med Biol Soc 2014;2014:2881–5.
    1. Chilali O, Puech P, Lakroum S, Diaf M, Mordon S, Betrouni N. Gland and Zonal Segmentation of Prostate on T2W MR Images. J Digit Imaging. 2016;29(6):730–6.
    1. Chu C, Oda M, Kitasaka T, Misawa K, Fujiwara M, Hayashi Y, et al. Multi-organ segmentation based on spatially-divided probabilistic atlas from 3D abdominal CT images. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):165–72.
    1. Clark T, Zhang J, Baig S, Wong A, Haider MA, Khalvati F. Fully automated segmentation of prostate whole gland and transition zone in diffusion-weighted MRI using convolutional neural networks. J Med Imaging (Bellingham). 2017;4(4):041307.
    1. Derraz F, Forzy G, Delebarre A, Taleb-Ahmed A, Oussalah M, Peyrodie L, et al. Prostate contours delineation using interactive directional active contours model and parametric shape prior model. Int J Numer Method Biomed Eng. 2015;31(11).
    1. Dong C, Chen YW, Foruzan AH, Lin L, Han XH, Tateyama T, et al. Segmentation of liver and spleen based on computational anatomy models. Comput Biol Med. 2015;67:146–60.
    1. Farag A, Le L, Roth HR, Liu J, Turkbey E, Summers RM. A Bottom-Up Approach for Pancreas Segmentation Using Cascaded Superpixels and (Deep) Image Patch Labeling. IEEE Trans Image Process. 2017;26(1):386–99.
    1. Fechter T, Adebahr S, Baltas D, Ben Ayed I, Desrosiers C, Dolz J. Esophagus segmentation in CT via 3D fully convolutional neural network and random walk. Med Phys. 2017;44(12):6341–52.
    1. Gauriau R, Cuingnet R, Lesage D, Bloch I. Multi-organ localization combining global-to-local regression and confidence maps. Med Image Comput Comput Assist Interv. 2014;17(Pt 3):337–44.
    1. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, et al. Automatic Multi-Organ Segmentation on Abdominal CT With Dense V-Networks. IEEE Trans Med Imaging. 2018;37(8):1822–34.
    1. Gloger O, Bulow R, Tonnies K, Volzke H. Automatic gallbladder segmentation using combined 2D and 3D shape features to perform volumetric analysis in native and secretin-enhanced MRCP sequences. MAGMA. 2018;31(3):383–97.
    1. Gloger O, Tonnies K, Bulow R, Volzke H. Automatized spleen segmentation in non-contrast-enhanced MR volume data using subject-specific shape priors. Phys Med Biol. 2017;62(14):5861–83.
    1. Gloger O, Tonnies K, Mensel B, Volzke H. Fully automatized renal parenchyma volumetry using a support vector machine based recognition system for subject-specific probability map generation in native MR volume data. Phys Med Biol. 2015;60(22):8675–93.
    1. Guo Y, Gao Y, Shao Y, Price T, Oto A, Shen D. Deformable segmentation of 3D MR prostate images via distributed discriminative dictionary and ensemble learning. Med Phys. 2014;41(7):072303.
    1. Hadjiiski L, Chan HP, Cohan RH, Caoili EM, Law Y, Cha K, et al. Urinary bladder segmentation in CT urography (CTU) using CLASS. Med Phys. 2013;40(11):111906.
    1. Hammon M, Cavallaro A, Erdt M, Dankerl P, Kirschner M, Drechsler K, et al. Model-based pancreas segmentation in portal venous phase contrast-enhanced CT images. J Digit Imaging. 2013;26(6):1082–90.
    1. He B, Huang C, Sharp G, Zhou S, Hu Q, Fang C, et al. Fast automatic 3D liver segmentation based on a three-level AdaBoost-guided active shape model. Med Phys. 2016;43(5):2421.
    1. Hu P, Wu F, Peng J, Bao Y, Chen F, Kong D. Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets. Int J Comput Assist Radiol Surg. 2017;12(3):399–411.
    1. Hu P, Wu F, Peng J, Liang P, Kong D. Automatic 3D liver segmentation based on deep learning and globally optimized surface evolution. Phys Med Biol. 2016;61(24):8676–98.
    1. Huo Y, Liu J, Xu Z, Harrigan RL, Assad A, Abramson RG, et al. Robust Multicontrast MRI Spleen Segmentation for Splenomegaly Using Multi-Atlas Segmentation. IEEE Trans Biomed Eng. 2018;65(2):336–43.
    1. Huynh HT, Karademir I, Oto A, Suzuki K. Computerized liver volumetry on MRI by using 3D geodesic active contour segmentation. AJR Am J Roentgenol. 2014;202(1):152–9.
    1. Huynh HT, Le-Trong N, Bao PT, Oto A, Suzuki K. Fully automated MR liver volumetry using watershed segmentation coupled with active contouring. Int J Comput Assist Radiol Surg. 2017;12(2):235–43.
    1. Ji H, He J, Yang X, Deklerck R, Cornelis J. ACM-based automatic liver segmentation from 3-D CT images by combining multiple atlases and improved mean-shift techniques. IEEE J Biomed Health Inform. 2013;17(3):690–8.
    1. Jia HX Y; Song Y; Cai W; Fulham M; Feng DD Atlas registration and ensemble deep convolutional neural network-based prostate segmentation using magnetic resonance imaging. Neurocomputing. 2018;31 January 2018;275():1358–1369.
    1. Jin C, Shi F, Xiang D, Jiang X, Zhang B, Wang X, et al. 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest. IEEE Trans Med Imaging. 2016;35(6):1395–407.
    1. Jin C, Shi F, Xiang D, Zhang L, Chen X. Fast segmentation of kidney components using random forests and ferns. Med Phys. 2017;44(12):6353–63.
    1. Karasawa K, Oda M, Kitasaka T, Misawa K, Fujiwara M, Chu C, et al. Multi-atlas pancreas segmentation: Atlas selection based on vessel structure. Med Image Anal. 2017;39:18–28.
    1. Ke Y, Changyang L, Xiuying W, Ang L, Yuchen Y, Dagan F, et al. Automatic prostate segmentation on MR images with deep network and graph model. Conf Proc IEEE Eng Med Biol Soc 2016;2016:635–8.
    1. Khalifa F, Soliman A, Elmaghraby A, Gimel’farb G, El-Baz A. 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models. Comput Math Methods Med. 2017;2017:9818506.
    1. Kim Y, Ge Y, Tao C, Zhu J, Chapman AB, Torres VE, et al. Automated Segmentation of Kidneys from MR Images in Patients with Autosomal Dominant Polycystic Kidney Disease. Clin J Am Soc Nephrol. 2016;11(4):576–84.
    1. Kline TL, Korfiatis P, Edwards ME, Blais JD, Czerwiec FS, Harris PC, et al. Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys. J Digit Imaging. 2017;30(4):442–8.
    1. Korsager AS, Fortunati V, van der Lijn F, Carl J, Niessen W, Ostergaard LR, et al. The use of atlas registration and graph cuts for prostate segmentation in magnetic resonance images. Med Phys. 2015;42(4):1614–24.
    1. Langerak TR, Berendsen FF, Van der Heide UA, Kotte AN, Pluim JP. Multiatlas-based segmentation with preregistration atlas selection. Med Phys. 2013;40(9):091701.
    1. Langerak TR, van der Heide UA, Kotte AN, Berendsen FF, van Vulpen M, Pluim JP. Expert-driven label fusion in multi-atlas-based segmentation of the prostate using weighted atlases. Int J Comput Assist Radiol Surg. 2013;8(6):929–36.
    1. Lavdas I, Glocker B, Kamnitsas K, Rueckert D, Mair H, Sandhu A, et al. Fully automatic, multiorgan segmentation in normal whole body magnetic resonance imaging (MRI), using classification forests (CFs), convolutional neural networks (CNNs), and a multi-atlas (MA) approach. Med Phys. 2017;44(10):5210–20.
    1. Li D, Liu L, Chen J, Li H, Yin Y, Ibragimov B, et al. Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours. Phys Med Biol. 2017;62(1):272–88.
    1. Li G, Chen X, Shi F, Zhu W, Tian J, Xiang D. Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images. IEEE Trans Image Process. 2015;24(12):5315–29.
    1. Liao M, Zhao YQ, Liu XY, Zeng YZ, Zou BJ, Wang XF, et al. Automatic liver segmentation from abdominal CT volumes using graph cuts and border marching. Comput Methods Programs Biomed. 2017;143:1–12.
    1. Liao S, Gao Y, Oto A, Shen D. Representation learning: a unified deep learning framework for automatic prostate MR segmentation. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):254–61.
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    1. Oh J, Martin DR, Hu X. Partitioned edge-function-scaled region-based active contour (p-ESRAC): automated liver segmentation in multiphase contrast-enhanced MRI. Med Phys. 2014;41(4):041914.
    1. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y. Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal. 2015;26(1):1–18.
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    1. Peng J, Hu P, Lu F, Peng Z, Kong D, Zhang H. 3D liver segmentation using multiple region appearances and graph cuts. Med Phys. 2015;42(12):6840–52.
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    1. Qiu W, Yuan J, Ukwatta E, Sun Y, Rajchl M, Fenster A. Fast globally optimal segmentation of 3D prostate MRI with axial symmetry prior. Med Image Comput Comput Assist Interv. 2013;16(Pt 2):198–205.
    1. Roth HR, Lu L, Lay N, Harrison AP, Farag A, Sohn A, et al. Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med Image Anal. 2018;45:94–107.
    1. Saito A, Nawano S, Shimizu A. Joint optimization of segmentation and shape prior from level-set-based statistical shape model, and its application to the automated segmentation of abdominal organs. Med Image Anal. 2016;28:46–65.
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    1. Shen J, Baum T, Cordes C, Ott B, Skurk T, Kooijman H, et al. Automatic segmentation of abdominal organs and adipose tissue compartments in water-fat MRI: Application to weight-loss in obesity. Eur J Radiol. 2016;85(9):1613–21.
    1. Song X, Cheng M, Wang B, Huang S, Huang X, Yang J. Adaptive fast marching method for automatic liver segmentation from CT images. Med Phys. 2013;40(9):091917.
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    1. Tomoshige S, Oost E, Shimizu A, Watanabe H, Nawano S. A conditional statistical shape model with integrated error estimation of the conditions; application to liver segmentation in non-contrast CT images. Med Image Anal. 2014;18(1):130–43.
    1. Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, et al. Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal. 2015;23(1):92–104.
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    1. Udupa JK, Odhner D, Zhao L, Tong Y, Matsumoto MM, Ciesielski KC, et al. Body-wide hierarchical fuzzy modeling, recognition, and delineation of anatomy in medical images. Med Image Anal. 2014;18(5):752–71.
    1. Wang J, Cheng Y, Guo C, Wang Y, Tamura S. Shape-intensity prior level set combining probabilistic atlas and probability map constrains for automatic liver segmentation from abdominal CT images. Int J Comput Assist Radiol Surg. 2016;11(5):817–26.
    1. Wang L, Li D, Huang S. An improved parallel fuzzy connected image segmentation method based on CUDA. Biomed Eng Online. 2016;15(1):56.
    1. Wolz R, Chu C, Misawa K, Fujiwara M, Mori K, Rueckert D. Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans Med Imaging. 2013;32(9):1723–30.
    1. Wu W, Zhou Z, Wu S, Zhang Y. Automatic Liver Segmentation on Volumetric CT Images Using Supervoxel-Based Graph Cuts. Comput Math Methods Med. 2016;2016:9093721.
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    1. Xu Z, Asman AJ, Shanahan PL, Abramson RG, Landman BA. SIMPLE is a good idea (and better with context learning). Med Image Comput Comput Assist Interv. 2014;17(Pt 1):364–71.
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    1. Xu Z, Gertz AL, Burke RP, Bansal N, Kang H, Landman BA, et al. Improving Spleen Volume Estimation Via Computer-assisted Segmentation on Clinically Acquired CT Scans. Acad Radiol. 2016;23(10):1214–20.
    1. Yang J, Haas B, Fang R, Beadle BM, Garden AS, Liao Z, et al. Atlas ranking and selection for automatic segmentation of the esophagus from CT scans. Phys Med Biol. 2017;62(23):9140–58.
    1. Yang X, Yang JD, Hwang HP, Yu HC, Ahn S, Kim BW, et al. Segmentation of liver and vessels from CT images and classification of liver segments for preoperative liver surgical planning in living donor liver transplantation. Comput Methods Programs Biomed. 2018;158:41–52.
    1. Yang X, Ye X, Slabaugh G. Multilabel region classification and semantic linking for colon segmentation in CT colonography. IEEE Trans Biomed Eng. 2015;62(3):948–59.
    1. Yoruk U, Hargreaves BA, Vasanawala SS. Automatic renal segmentation for MR urography using 3D-GrabCut and random forests. Magn Reson Med. 2018;79(3):1696–707.
    1. Zhang P, Liang Y, Chang S, Fan H. Kidney segmentation in CT sequences using graph cuts based active contours model and contextual continuity. Med Phys. 2013;40(8):081905.
    1. Zheng Y, Ai D, Mu J, Cong W, Wang X, Zhao H, et al. Automatic liver segmentation based on appearance and context information. Biomed Eng Online. 2017;16(1):16.
    1. Zhou X, Takayama R, Wang S, Hara T, Fujita H. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method. Med Phys. 2017;44(10):5221–33.
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.
    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.
    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.
    1. Athertya JS, Saravana Kumar G. Automatic segmentation of vertebral contours from CT images using fuzzy corners. Comput Biol Med. 2016;72:75–89.
    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.
    1. Carballido-Gamio J, Bonaretti S, Saeed I, Harnish R, Recker R, Burghardt AJ, et al. Automatic multi-parametric quantification of the proximal femur with quantitative computed tomography. Quant Imaging Med Surg. 2015;5(4):552–68.
    1. Castro-Mateos I, Pozo JM, Eltes PE, Rio LD, Lazary A, Frangi AF. 3D segmentation of annulus fibrosus and nucleus pulposus from T2-weighted magnetic resonance images. Phys Med Biol. 2014;59(24):7847–64.
    1. Chandra SS, Xia Y, Engstrom C, Crozier S, Schwarz R, Fripp J. Focused shape models for hip joint segmentation in 3D magnetic resonance images. Med Image Anal. 2014;18(3):567–78.
    1. Chandra SS, Surowiec R, Ho C, Xia Y, Engstrom C, Crozier S, Fripp J. Automated analysis of hip joint cartilage combining MR T2 and three-dimensional fast-spin-echo images. Magn Reson Med. 2016. January;75(1):403–13. doi: 10.1002/mrm.25598.
    1. Chen C, Belavy D, Yu W, Chu C, Armbrecht G, Bansmann M, et al. Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation. IEEE Trans Med Imaging. 2015;34(8):1719–29.
    1. Chen F, Liu J, Zhao Z, Zhu M, Liao H. Three-Dimensional Feature-Enhanced Network for Automatic Femur Segmentation. IEEE J Biomed Health Inform. 2019;23(1):243–52.
    1. Chu C, Bai J, Wu X, Zheng G. MASCG: Multi-Atlas Segmentation Constrained Graph method for accurate segmentation of hip CT images. Med Image Anal. 2015;26(1):173–84.
    1. Chu C, Belavy DL, Armbrecht G, Bansmann M, Felsenberg D, Zheng G. Fully Automatic Localization and Segmentation of 3D Vertebral Bodies from CT/MR Images via a Learning-Based Method. PLoS One. 2015;10(11):e0143327.
    1. Chu C, Chen C, Liu L, Zheng G. FACTS: Fully Automatic CT Segmentation of a Hip Joint. Ann Biomed Eng. 2015;43(5):1247–59.
    1. Dam EB, Lillholm M, Marques J, Nielsen M. Automatic segmentation of high- and low-field knee MRIs using knee image quantification with data from the osteoarthritis initiative. J Med Imaging (Bellingham). 2015. April;2(2):024001. doi: 10.1117/1.JMI.2.2.024001.
    1. Gadermayr M, Disch C, Müller M, Merhof D, Gess B. A comprehensive study on automated muscle segmentation for assessing fat infiltration in neuromuscular diseases. Magn Reson Imaging. 2018. May;48:20–26. doi: 10.1016/j.mri.2017.12.014.
    1. Gaonkar B, Xia Y, Villaroman DS, Ko A, Attiah M, Beckett JS, et al. Multi-Parameter Ensemble Learning for Automated Vertebral Body Segmentation in Heterogeneously Acquired Clinical MR Images. IEEE J Transl Eng Health Med. 2017;5:1800412.
    1. Ghosh S, Chaudhary V. Supervised methods for detection and segmentation of tissues in clinical lumbar MRI. Comput Med Imaging Graph. 2014;38(7):639–49.
    1. Hanaoka S, Masutani Y, Nemoto M, Nomura Y, Miki S, Yoshikawa T, et al. Landmark-guided diffeomorphic demons algorithm and its application to automatic segmentation of the whole spine and pelvis in CT images. Int J Comput Assist Radiol Surg. 2017;12(3):413–30.
    1. Huang J, Jian F, Wu H, Li H. An improved level set method for vertebra CT image segmentation. Biomed Eng Online. 2013;12:48.
    1. Huang J, Griffith JF, Wang D, Shi L. Graph-Cut-Based Segmentation of Proximal Femur from Computed Tomography Images with Shape Prior. Journal of Medical and Biological Engineering. 2015;35(5):594–607.
    1. Karlsson A, Rosander J, Romu T, Tallberg J, Grönqvist A, Borga M, Dahlqvist Leinhard O. Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water-fat MRI. J Magn Reson Imaging. 2015. June;41(6):1558–69. doi: 10.1002/jmri.24726.
    1. Kashyap S, Oguz I, Zhang H, Sonka M. Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative. Med Image Comput Comput Assist Interv. 2016. October;9901:344–351. doi: 10.1007/978-3-319-46723-8_40.
    1. Koh J, Chaudhary V, Jeon EK, Dhillon G. Automatic spinal canal detection in lumbar MR images in the sagittal view using dynamic programming. Comput Med Imaging Graph. 2014;38(7):569–79.
    1. Korez R, Ibragimov B, Likar B, Pernus F, Vrtovec T. A Framework for Automated Spine and Vertebrae Interpolation-Based Detection and Model-Based Segmentation. IEEE Trans Med Imaging. 2015;34(8):1649–62.
    1. Lareau-Trudel E, Le Troter A, Ghattas B, Pouget J, Attarian S, Bendahan D, Salort-Campana E. Muscle Quantitative MR Imaging and Clustering Analysis in Patients with Facioscapulohumeral Muscular Dystrophy Type 1. PLoS One. 2015. July 16;10(7):e0132717. doi: 10.1371/journal.pone.0132717.
    1. Le Troter A, Fouré A, Guye M, Confort-Gouny S, Mattei JP, Gondin J, Salort-Campana E, Bendahan D. Volume measurements of individual muscles in human quadriceps femoris using atlas-based segmentation approaches. MAGMA. 2016. April;29(2):245–57. doi: 10.1007/s10334-016-0535-6.
    1. Lee H, Troschel FM, Tajmir S, Fuchs G, Mario J, Fintelmann FJ, Do S. Pixel-Level Deep Segmentation: Artificial Intelligence Quantifies Muscle on Computed Tomography for Body Morphometric Analysis. J Digit Imaging. 2017. August;30(4):487–498. doi: 10.1007/s10278-017-9988-z.
    1. Lee JG, Gumus S, Moon CH, Kwoh CK, Bae KT. Fully automated segmentation of cartilage from the MR images of knee using a multi-atlas and local structural analysis method. Med Phys. 2014. September;41(9):092303. doi: 10.1118/1.4893533.
    1. Li X, Dou Q, Chen H, Fu CW, Qi X, Belavy DL, et al. 3D multi-scale FCN with random modality voxel dropout learning for Intervertebral Disc Localization and Segmentation from Multi-modality MR Images. Med Image Anal. 2018;45:41–54.
    1. Liu F, Zhou Z, Jang H, Samsonov A, Zhao G, Kijowski R. Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med. 2018. April;79(4):2379–2391. doi: 10.1002/mrm.26841.
    1. Liu H, Zhao J, Dai N, Qian H, Tang Y. Improve accuracy for automatic acetabulum segmentation in CT images. Biomed Mater Eng. 2014;24(6):3159–77.
    1. Liu S, Xie Y, Reeves AP. Automated 3D closed surface segmentation: application to vertebral body segmentation in CT images. Int J Comput Assist Radiol Surg. 2016;11(5):789–801.
    1. Makrogiannis S, Boukari F, Ferrucci L. Automated skeletal tissue quantification in the lower leg using peripheral quantitative computed tomography. Physiol Meas. 2018. April 3;39(3):035011. doi: 10.1088/1361-6579/aaafb5.
    1. Makrogiannis S, Fishbein KW, Moore AZ, Spencer RG, Ferrucci L. Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization. IEEE Trans Biomed Eng. 2016. April;63(4):805–13. doi: 10.1109/TBME.2015.2474305.
    1. Mendoza CS, Safdar N, Okada K, Myers E, Rogers GF, Linguraru MG. Personalized assessment of craniosynostosis via statistical shape modeling. Med Image Anal. 2014;18(4):635–46.
    1. Neubert A, Yang Z, Engstrom C, Xia Y, Strudwick MW, Chandra SS, Fripp J, Crozier S. Automatic segmentation of the glenohumeral cartilages from magnetic resonance images. Medical physics. 2016. October 1;43(10):5370–9.
    1. Onal S, Chen X, Lai-Yuen S, Hart S. Automatic vertebra segmentation on dynamic magnetic resonance imaging. J Med Imaging (Bellingham). 2017;4(1):014504.
    1. Öztürk CN, Albayrak S. Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling. Comput Biol Med. 2016. May 1;72:90–107. doi: 10.1016/j.compbiomed.2016.03.011.
    1. Paproki A, Engstrom C, Chandra SS, Neubert A, Fripp J, Crozier S. Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images--data from the Osteoarthritis Initiative. Osteoarthritis Cartilage. 2014. September;22(9):1259–70. doi: 10.1016/j.joca.2014.06.029.
    1. Paproki A, Engstrom C, Strudwick M, Wilson KJ, Surowiec RK, Ho C, Crozier S, Fripp J. Automated T2-mapping of the Menisci From Magnetic Resonance Images in Patients with Acute Knee Injury. Acad Radiol. 2017. October;24(10):1295–1304. doi: 10.1016/j.acra.2017.03.025.
    1. Pedoia V, Li X, Su F, Calixto N, Majumdar S. Fully automatic analysis of the knee articular cartilage T1ρ relaxation time using voxel-based relaxometry. J Magn Reson Imaging. 2016. April;43(4):970–80. doi: 10.1002/jmri.25065.
    1. Prasoon A, Igel C, Loog M, Lauze F, Dam EB, Nielsen M. Femoral cartilage segmentation in knee MRI scans using two stage voxel classification. Conf Proc IEEE Eng Med Biol Soc 2013;2013:5469–72. doi: 10.1109/EMBC.2013.6610787.
    1. 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):246–53. PubMed PMID: 24579147.
    1. Ramme AJ, Guss MS, Vira S, Vigdorchik JM, Newe A, Raithel E, Chang G. Evaluation of Automated Volumetric Cartilage Quantification for Hip Preservation Surgery. J Arthroplasty. 2016. January;31(1):64–9. doi: 10.1016/j.arth.2015.08.009.
    1. Ruiz-Espana S, Domingo J, Diaz-Parra A, Dura E, D’Ocon-Alcaniz V, Arana E, et al. Automatic segmentation of the spine by means of a probabilistic atlas with a special focus on ribs suppression. Med Phys. 2017;44(9):4695–707.
    1. Shan L, Zach C, Charles C, Niethammer M. Automatic atlas-based three-label cartilage segmentation from MR knee images. Med Image Anal. 2014. October;18(7):1233–46. doi: 10.1016/j.media.2014.05.008.
    1. Thomas MS, Newman D, Leinhard OD, Kasmai B, Greenwood R, Malcolm PN, Karlsson A, Rosander J, Borga M, Toms AP. Test-retest reliability of automated whole body and compartmental muscle volume measurements on a wide bore 3T MR system. Eur Radiol. 2014. September;24(9):2279–91. doi: 10.1007/s00330-014-3226-6.
    1. Wlodarczyk J, Czaplicka K, Tabor Z, Wojciechowski W, Urbanik A. Segmentation of bones in magnetic resonance images of the wrist. Int J Comput Assist Radiol Surg. 2015;10(4):419–31.
    1. Wu D, Sofka M, Birkbeck N, Zhou SK. Segmentation of multiple knee bones from CT for orthopedic knee surgery planning. Med Image Comput Comput Assist Interv. 2014;17(Pt 1):372–80.
    1. Xia Y, Fripp J, Chandra SS, Schwarz R, Engstrom C, Crozier S. Automated bone segmentation from large field of view 3D MR images of the hip joint. Phys Med Biol. 2013;58(20):7375–90.
    1. Xia Y, Chandra SS, Engstrom C, Strudwick MW, Crozier S, Fripp J. Automatic hip cartilage segmentation from 3D MR images using arc-weighted graph searching. Phys Med Biol. 2014. December 7;59(23):7245–66. doi: 10.1088/0031-9155/59/23/7245.
    1. Xin C, Graham J, Hutchinson C, Muir L. Automatic generation of statistical pose and shape models for articulated joints. IEEE Trans Med Imaging. 2014;33(2):372–83.
    1. Yang YX, Chong MS, Lim WS, Tay L, Yew S, Yeo A, Tan CH. Validity of estimating muscle and fat volume from a single MRI section in older adults with sarcopenia and sarcopenic obesity. Clin Radiol. 2017. May;72(5):427.e9–427.e14. doi:10.1016/j.crad.2016.12.011.
    1. Yang Z, Fripp J, Chandra SS, Neubert A, Xia Y, Strudwick M, et al. Automatic bone segmentation and bone-cartilage interface extraction for the shoulder joint from magnetic resonance images. Phys Med Biol. 2015;60(4):1441–59.
    1. Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P, et al. A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph. 2016;49:16–28.
    1. Zheng Q, Lu Z, Feng Q, Ma J, Yang W, Chen C, et al. Adaptive segmentation of vertebral bodies from sagittal MR images based on local spatial information and Gaussian weighted chi-square distance. J Digit Imaging. 2013;26(3):578–93.
    1. Zheng G, Chu C, Belavy DL, Ibragimov B, Korez R, Vrtovec T, et al. Evaluation and comparison of 3D intervertebral disc localization and segmentation methods for 3D T2 MR data: A grand challenge. Med Image Anal. 2017;35:327–44.
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).
    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.
    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.
    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.
    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.
    1. Ertas G, Doran SJ, Leach MO. A computerized volumetric segmentation method applicable to multicentre MRI data to support computer-aided breast tissue analysis, density assessment and lesion localization. Medical & biological engineering & computing. 2017. January 1;55(1):57–68.
    1. Fooladivanda A, Shokouhi SB, Ahmadinejad N. Localized-atlas-based segmentation of breast MRI in a decision-making framework. Australasian physical & engineering sciences in medicine. 2017. March 1;40(1):69–84.
    1. Gubern-Merida A, Kallenberg M, Mann RM, Marti R, Karssemeijer N. Breast segmentation and density estimation in breast MRI: a fully automatic framework. IEEE journal of biomedical and health informatics. 2015. January;19(1):349–57.
    1. Hu L, Cheng Z, Wang M, Song Z. Image manifold revealing for breast lesion segmentation in DCE-MRI. Bio-medical materials and engineering. 2015. January 1;26(s1):S1353–60.
    1. Ivanovska T, Laqua R, Wang L, Liebscher V, Völzke H, Hegenscheid K. A level set based framework for quantitative evaluation of breast tissue density from MRI data. PloS one. 2014. November 25;9(11):e112709.
    1. Janaki SD, Geetha K. Automatic segmentation of lesion from breast DCE-MR image using artificial fish swarm optimization algorithm. Polish Journal of Medical Physics and Engineering. 2017. June 27;23(2):29–36.
    1. Jiang L, Hu X, Xiao Q, Gu Y, Li Q. Fully automated segmentation of whole breast using dynamic programming in dynamic contrast enhanced MR images. Medical physics. 2017. June;44(6):2400–14.
    1. Khalvati F, Gallego-Ortiz C, Balasingham S, Martel AL. Automated segmentation of breast in 3-D MR images using a robust atlas. IEEE transactions on medical imaging. 2015. January;34(1):116–25.
    1. Lin M, Chen JH, Wang X, Chan S, Chen S, Su MY. Template based automatic breast segmentation on MRI by excluding the chest region. Medical physics. 2013. December 1;40(12).
    1. Milenković J, Chambers O, Mušič MM, Tasič JF. Automated breast-region segmentation in the axial breast MR images. Computers in biology and medicine. 2015. July 1;62:55–64.
    1. Tagliafico A, Bignotti B, Tagliafico G, Tosto S, Signori A, Calabrese M. Quantitative evaluation of background parenchymal enhancement (BPE) on breast MRI. A feasibility study with a semi-automatic and automatic software compared to observer-based scores. The British journal of radiology. 2015. December;88(1056):20150417.
    1. Thakran S, Chatterjee S, Singhal M, Gupta RK, Singh A. Automatic outer and inner breast tissue segmentation using multi-parametric MRI images of breast tumor patients. PloS one. 2018. January 10;13(1):e0190348.
    1. Wengert GJ, Helbich TH, Vogl WD, Baltzer P, Langs G, Weber M, Bogner W, Gruber S, Trattnig S, Pinker K. Introduction of an Automated User–Independent Quantitative Volumetric Magnetic Resonance Imaging Breast Density Measurement System Using the Dixon Sequence: Comparison With Mammographic Breast Density Assessment. Investigative radiology. 2015. February 1;50(2):73–80.
    1. Wengert GJ, Pinker Domenig K, Helbich TH, Vogl WD, Clauser P, Bickel H, Marino MA, Magometschnigg HF, Baltzer PA. Influence of fat–water separation and spatial resolution on automated volumetric MRI measurements of fibroglandular breast tissue. NMR in Biomedicine. 2016. June;29(6):702–8.
    1. Wu S, Weinstein SP, Conant EF, Kontos D. Automated fibroglandular tissue segmentation and volumetric density estimation in breast MRI using an atlas aided fuzzy C means method. Medical physics. 2013. December 1;40(12).
B6. Adipose Tissue Segmentation
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