Skip to main content
SpringerLink
Log in

Advances on pancreas segmentation: a review

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Accurate pancreas segmentation from medical images is an important yet challenging problem for medical image analysis and medical surgery. Challenges relating to the pancreas image acquisition, the limited availability of image data and segmentation methodology hinder this segmentation task. This paper aims to present a systematic review of the different methodologies. Recently, a variety of segmentation methods have been proposed for automatic delineation of pancreas images. We intended to survey the methods proposed for image segmentation, outline those that have already been adopted for pancreas segmentation, perform a comparison between them using various indices and experimental data, and discuss their major contributions. Looking at the theoretical approach, different segmentation methods have been applied for delineating the pancreas and can be classified into those based on region, edge, atlas, neural network and other categories. Each segmentation method has its own advantages and disadvantages. Based on the previously performed analysis and discussion, and compared to other abdominal organs, improving the results of pancreas segmentation in the near future will require addressing several challenging issues. These issues include: (1) establishing publically available standardized datasets; (2) define clear metrics to evaluate the segmentation performance and (3) design new segmentation methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Siegel RL, Miller KD, Jemal A (2017) Cancer statistics, 2017. CA Cancer J Clin 67(1):7–30. https://doi.org/10.3322/caac.21387

    Article  Google Scholar 

  2. Chen W, Zheng R, Baade PD, Zhang S, Zeng H, Bray F, Jemal A, Yu XQ, He J (2016) Cancer statistics in China, 2015. CA Cancer J Clin 66(2):115–132. https://doi.org/10.3322/caac.21338

    Article  Google Scholar 

  3. Roth HR, Lu L, Lay N, Harrison AP, Farag A, Sohn A, Summers RM (2018) Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation. Med Image Anal 45:94–107. https://doi.org/10.1016/j.media.2018.01.006

    Article  Google Scholar 

  4. Gibson E, Giganti F, Hu Y, Bonmati E, Bandula S, Gurusamy K, Davidson B, Pereira SP, Clarkson MJ, Barratt DC (2018) Automatic multi-organ segmentation on abdominal CT with dense V-networks. IEEE Trans Med Imaging 37(8):1822–1834. https://doi.org/10.1109/TMI.2018.2806309

    Article  Google Scholar 

  5. Borges VRP, de Oliveira MCF, Silva TG, Vieira AAH, Hamann B (2018) Region growing for segmenting green microalgae images. IEEE/ACM Trans Comput Biol Bioinform 15(1):257–270. https://doi.org/10.1109/Tcbb.2016.2615606

    Article  Google Scholar 

  6. Hao R, Qiang Y, Yan X (2018) Juxta-vascular pulmonary nodule segmentation in PET-CT imaging based on an LBF active contour model with information entropy and joint vector. Comput Math Methods Med 2018:2183847. https://doi.org/10.1155/2018/2183847

    Article  MathSciNet  MATH  Google Scholar 

  7. Latha M, Kavitha G (2018) Segmentation and texture analysis of structural biomarkers using neighborhood-clustering-based level set in MRI of the schizophrenic brain. Magma 31(4):483–499. https://doi.org/10.1007/s10334-018-0674-z

    Article  Google Scholar 

  8. Huang Q, Ding H, Wang X, Wang G (2018) Fully automatic liver segmentation in CT images using modified graph cuts and feature detection. Comput Biol Med 95:198–208. https://doi.org/10.1016/j.compbiomed.2018.02.012

    Article  Google Scholar 

  9. Saiviroonporn P, Korpraphong P, Viprakasit V, Krittayaphong R (2018) An automated segmentation of R2* iron-overloaded liver images using a fuzzy C-mean clustering scheme. J Comput Assist Tomogr 42(3):387–398. https://doi.org/10.1097/RCT.0000000000000713

    Article  Google Scholar 

  10. Yang J, Gui Z, Zhang L, Zhang P (2018) Aperture generation based on threshold segmentation for intensity modulated radiotherapy treatment planning. Med Phys 45(4):1758–1770. https://doi.org/10.1002/mp.12819

    Article  Google Scholar 

  11. Wu Z, Guo Y, Park SH, Gao Y, Dong P, Lee SW, Shen D (2018) Robust brain ROI segmentation by deformation regression and deformable shape model. Med Image Anal 43:198–213. https://doi.org/10.1016/j.media.2017.11.001

    Article  Google Scholar 

  12. Liu F, Zhou Z, Jang H, Samsonov A, Zhao G, Kijowski R (2018) Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging. Magn Reson Med 79(4):2379–2391. https://doi.org/10.1002/mrm.26841

    Article  Google Scholar 

  13. Summers RM (2016) Progress in fully automated abdominal CT interpretation. AJR Am J Roentgenol 207(1):67–79. https://doi.org/10.2214/AJR.15.15996

    Article  Google Scholar 

  14. Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM (2016) A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling. IEEE Trans Image Process 26(1):386–399. https://doi.org/10.1109/TIP.2016.2624198

    Article  MathSciNet  MATH  Google Scholar 

  15. Masahiro Oda NS, Kenichi Karasawa, Yukitaka Nimura, Takayuki Kitasaka, Kazunari Misawa, Michitaka Fujiwara, Daniel Rueckert, and Kensaku Mori (2016) Regression forest-based atlas localization and direction specific atlas generation for pancreas segmentation. Paper presented at the MICCAI, Athens, Oct 17–21

  16. Li ZC, Tang JH, He XF (2018) Robust structured nonnegative matrix factorization for image representation. IEEE Trans Neural Netw Learn Syst 29(5):1947–1960. https://doi.org/10.1109/Tnnls.2017.2691725

    Article  MathSciNet  Google Scholar 

  17. Li ZC, Tang JH (2015) Unsupervised feature selection via nonnegative spectral analysis and redundancy control. IEEE Trans Image Process 24(12):5343–5355. https://doi.org/10.1109/Tip.2015.2479560

    Article  MathSciNet  MATH  Google Scholar 

  18. Shimizu A, Kimoto T, Kobatake H, Nawano S, Shinozaki K (2010) Automated pancreas segmentation from three-dimensional contrast-enhanced computed tomography. Int J Comput Assist Radiol Surg 5(1):85–98. https://doi.org/10.1007/s11548-009-0384-0

    Article  Google Scholar 

  19. Farag A, Lu L, Turkbey E, Liu JM, Summers RM (2014) A bottom-up approach for automatic pancreas segmentation in abdominal CT scans. Lect Notes Comput Sci 8676:103–113. https://doi.org/10.1007/978-3-319-13692-9_10

    Article  Google Scholar 

  20. Rajput GG, Chavan AM (2016) Automatic detection of abnormalities associated with abdomen and liver images: a survey on segmentation methods. Paper presented at the International Journal of Computer Applications, Florence, Oct 23–26

  21. Torres HR, Queiros S, Morais P, Oliveira B, Fonseca JC, Vilaca JL (2018) Kidney segmentation in ultrasound, magnetic resonance and computed tomography images: a systematic review. Comput Methods Prog Biomed 157:49–67. https://doi.org/10.1016/j.cmpb.2018.01.014

    Article  Google Scholar 

  22. Tam TD, Binh NT (2015) Efficient pancreas segmentation in computed tomography based on region-growing. Lect Notes Inst Comp Sci Soc Infrom Telecommun Eng 144:332–340. https://doi.org/10.1007/978-3-319-15392-6_31

    Article  Google Scholar 

  23. Ait Ibachir I, Es-salhi R, Daoudi I, Tallal S, Medromi H (2017) A survey on segmentation techniques of mammogram images. In: El-Azouzi R et al (eds) Advances in ubiquitous networking 2, Lecture notes in electrical engineering, vol 397, pp 545–556. https://doi.org/10.1007/978-981-10-1627-1_43

    Chapter  Google Scholar 

  24. Farag A, Lu L, Roth HR, Liu J, Turkbey E, Summers RM (2017) Automatic pancreas segmentation using coarse-to-fine Superpixel labeling. In: Lu L et al (eds) Deep learning and convolutional neural networks for medical image computing, Advances in computer vision and pattern recognition. Springer, Cham, pp 279–302. https://doi.org/10.1007/978-3-319-42999-1_16

    Chapter  Google Scholar 

  25. Dhanachandra N, Chanu YJ (2017) A survey on image segmentation methods using clustering techniques. Eur J Eng Res Sci 65(1):797–806. https://doi.org/10.1016/j.procs.2015.09.027

    Article  Google Scholar 

  26. Nilakant R, Menon HP, Vikram K (2016) A survey on advanced segmentation techniques for brain MRI image segmentation. Paper presented at the Intelligent Systems Technologies and Application, Jaipur, Sep 21–24

  27. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak J, van Ginneken B, Sanchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

    Article  Google Scholar 

  28. Howard M, Hock MC, Meehan BT, Dresselhaus-Cooper LE (2018) A locally adapting technique for edge detection using image segmentation. SIAM J Sci Comput 40(4):B1161–B1179. https://doi.org/10.1137/17m1155363

    Article  MathSciNet  MATH  Google Scholar 

  29. Gui L, Yang X (2018) Automatic renal lesion segmentation in ultrasound images based on saliency features, improved LBP, and an edge indicator under level set framework. Med Phys 45(1):223–235. https://doi.org/10.1002/mp.12661

    Article  Google Scholar 

  30. Kozegar E, Soryani M, Behnam H, Salamati M, Tan T (2018) Mass segmentation in automated 3-D breast ultrasound using adaptive region growing and supervised edge-based deformable model. IEEE Trans Med Imaging 37(4):918–928. https://doi.org/10.1109/TMI.2017.2787685

    Article  Google Scholar 

  31. Iglesias JE, Sabuncu MR (2015) Multi-atlas segmentation of biomedical images: a survey. Med Image Anal 24(1):205–219. https://doi.org/10.1016/j.media.2015.06.012

    Article  Google Scholar 

  32. Li B, Panda S, Xu Z (2013) Regression forest region recognition enhances multi-atlas spleen labeling. Paper presented at the international conference on Information Processing in Medical Imaging, Nagoya, Sep 22–26

  33. Karasawa K, Oda M, Hayashi Y, Nimura Y, Kitasaka T, Misawa K, Fujiwara M, Rueckert D, Mori K (2015) Pancreas segmentation from 3D abdominal CT images using patient-specific weighted subspatial probabilistic atlases. Paper presented at the Medical Imaging 2015: Image Processing, Orlando, Feb 24–26

  34. Doshi J, Erus G, Ou Y, Gaonkar B, Davatzikos C (2013) Multi-atlas skull-stripping. Acad Radiol 20(12):1566–1576

    Article  Google Scholar 

  35. Janes AC, Park MT, Farmer S, Chakravarty MM (2015) Striatal morphology is associated with tobacco cigarette craving. Neuropsychopharmacology 40(2):406–411

    Article  Google Scholar 

  36. Wang H, Yushkevich PA (2013) Groupwise segmentation with multi-atlas joint label fusion. Paper presented at the MICCAI, Nagoya, Sep 22–26

  37. Kotrotsou A, Bennett DA, Schneider JA, Dawe RJ, Golak T, Leurgans SE, Yu L, Arfanakis K (2014) Ex vivo MR volumetry of human brain hemispheres. Magn Reson Med 71(1):364–374. https://doi.org/10.1002/mrm.24661

    Article  Google Scholar 

  38. Yang J, Zhang Y, Zhang L, Dong L (2010) Automatic Segmentation of Parotids from CT Scans Using Multiple Atlases. Paper presented at the MICCAI, Beijing, Sep 20–24

  39. Iglesias JE, Karssemeijer N (2009) Robust initial detection of landmarks in film-screen mammograms using multiple FFDM atlases. IEEE Trans Med Imaging 28(11):1815–1824

    Article  Google Scholar 

  40. Isgum I, Staring M, Rutten A, Prokop M, Viergever MA, Van GB (2009) Multi-atlas-based segmentation with local decision fusion – application to cardiac and aortic segmentation in CT scans. IEEE Trans Med Imaging 28(7):1000–1010

    Article  Google Scholar 

  41. Jia H, Yap PT, Shen D (2012) Iterative multi-atlas-based multi-image segmentation with tree-based registration. Neuroimage 59(1):422–430

    Article  Google Scholar 

  42. Aljabar P, Heckemann RA, Hammers A, Hajnal JV, Rueckert D (2009) Multi-atlas based segmentation of brain images: atlas selection and its effect on accuracy. Neuroimage 46(3):726–738. https://doi.org/10.1016/j.neuroimage.2009.02.018

    Article  Google Scholar 

  43. Shi Y, Lai R, Toga AW Conformal mapping via metric optimization with application for cortical label fusion. International conference on Information Processing in Medical Imaging, Asilomar, Jun 28–Jul 3, 2013. p 244

  44. Sjoberg C, Johansson S, Ahnesjo A (2014) How much will linked deformable registrations decrease the quality of multi-atlas segmentation fusions? Radiat Oncol 9:251. https://doi.org/10.1186/s13014-014-0251-1

    Article  Google Scholar 

  45. Jorge Cardoso M, Leung K, Modat M, Keihaninejad S, Cash D, Barnes J, Fox NC, Ourselin S, Alzheimer’s Disease Neuroimaging I (2013) STEPS: similarity and truth estimation for propagated segmentations and its application to hippocampal segmentation and brain parcelation. Med Image Anal 17(6):671–684. https://doi.org/10.1016/j.media.2013.02.006

    Article  Google Scholar 

  46. Yang JZ, Haas B, Fang R, Beadle BM, Garden AS, Liao ZX, Zhang LF, Balter P, Court L (2017) Atlas ranking and selection for automatic segmentation of the esophagus from CT scans. Phys Med Biol 62(23):9140–9158. https://doi.org/10.1088/1361-6560/aa94ba

    Article  Google Scholar 

  47. Ta VT, Giraud R, Collins DL, Coupe P (2014) Optimized patchmatch for near real time and accurate label fusion. Paper presented at the MICCAI, Boston, Sep 14–18

  48. Langerak TR, Berendsen FF, Van der Heide UA, Kotte AN, Pluim JP (2013) Multiatlas-based segmentation with preregistration atlas selection. Med Phys 40(9):091701. https://doi.org/10.1118/1.4816654

    Article  Google Scholar 

  49. Sabuncu MR, Yeo BTT, Van Leemput K, Fischl B, Golland P (2010) A generative model for image segmentation based on label fusion. IEEE Trans Med Imaging 29(10):1714–1729. https://doi.org/10.1109/Tmi.2010.2050897

    Article  Google Scholar 

  50. Xu Z, Li B, Panda S, Asman AJ, Merkle KL, Shanahan PL, Abramson RG, Landman BA (2014) Shape-constrained multi-atlas segmentation of spleen in CT. Proc SPIE Int Soc Opt Eng 9034:903446. https://doi.org/10.1117/12.2043079

    Article  Google Scholar 

  51. Zikic D, Glocker B, Criminisi A (2014) Encoding atlases by randomized classification forests for efficient multi-atlas label propagation. Med Image Anal 18(8):1262–1273. https://doi.org/10.1016/j.media.2014.06.010

    Article  Google Scholar 

  52. Yan M, Liu H, Xu XY, Song EM, Qian YJ, Pan N, Jin RC, Jin LH, Cheng SR, Hung CC (2017) An improved label fusion approach with sparse patch-based representation for MRI brain image segmentation. Int J Imag Syst Tech 27(1):23–32. https://doi.org/10.1002/ima.22207

    Article  Google Scholar 

  53. Yan M, Liu H, Song EM, Qian YJ, Jin LH, Hung CC (2018) Sparse patch-based representation with combined information of atlas for multi-atlas label fusion. IET Image Process 12(8):1345–1353. https://doi.org/10.1049/iet-ipr.2017.1108

    Article  Google Scholar 

  54. Huo J, Wang G, Wu QMJ, Thangarajah A (2015) Label fusion for multi-atlas segmentation based on majority voting. Paper presented at the International Conference Image Analysis and Recognition, Póvoa de Varzim, Jul 13–15

  55. Zaffino P, Ciardo D, Raudaschl P, Fritscher K, Ricotti R, Alterio D, Marvaso G, Fodor C, Baroni G, Amato F, Orecchia R, Jereczek-Fossa BA, Sharp GC, Spadea MF (2018) Multi atlas based segmentation: should we prefer the best atlas group over the group of best atlases? Phys Med Biol 63(12). https://doi.org/10.1088/1361-6560/aac712. ARTN 12NT01

  56. Tor-Diez C, Passat N, Bloch I, Faisan S, Bednarek N, Rousseau F (2018) An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Comput Med Imaging Grap 70:73–82. https://doi.org/10.1016/j.compmedimag.2018.09.003

    Article  Google Scholar 

  57. Fritscher KD, Peroni M, Zaffino P, Spadea MF, Schubert R, Sharp G (2014) Automatic segmentation of head and neck CT images for radiotherapy treatment planning using multiple atlases, statistical appearance models, and geodesic active contours. Med Phys 41(5):051910

    Article  Google Scholar 

  58. Nouranian S, Mahdavi SS, Spadinger I, Morris WJ, Salcudean SE, Abolmaesumi P (2015) A multi-atlas-based segmentation framework for prostate brachytherapy. IEEE Trans Med Imaging 34(4):950–961. https://doi.org/10.1109/Tmi.2014.2371823

    Article  Google Scholar 

  59. Li ZC, Tang JH (2015) Weakly supervised deep metric learning for community-contributed image retrieval. IEEE Trans Multimedia 17(11):1989–1999. https://doi.org/10.1109/Tmm.2015.2477035

    Article  Google Scholar 

  60. Wang XF, Lee F, Chen Q (2019) Similarity-preserving hashing based on deep neural networks for large-scale image retrieval. J Vis Commun Image Represent 61:260–271. https://doi.org/10.1016/j.jvcir.2019.03.024

    Article  Google Scholar 

  61. Li Z, Tang J, Mei T (2018) Deep collaborative embedding for social image understanding. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2018.2852750

  62. Li ZC, Tang JH (2017) Weakly supervised deep matrix factorization for social image understanding. IEEE Trans Image Process 26(1):276–288. https://doi.org/10.1109/Tip.2016.2624140

    Article  MathSciNet  MATH  Google Scholar 

  63. Fu M, Wu W, Hong X, Liu Q, Jiang J, Ou Y, Zhao Y, Gong X (2018) Hierarchical combinatorial deep learning architecture for pancreas segmentation of medical computed tomography cancer images. BMC Syst Biol 12(Suppl 4):56. https://doi.org/10.1186/s12918-018-0572-z

    Article  Google Scholar 

  64. Roth HR, Lu L, Farag A, Shin HC, Liu JM, Turkbey EB, Summers RM (2015) DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. Paper presented at the MICCAI, Munich, Oct 5–9

  65. Christ PF, Elshaer MEA, Ettlinger F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Armbruster M, Hofmann F, D’Anastasi M (2016) Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. Paper presented at the MICCAI, Athens, Oct 17–21

  66. Vorontsov E, Tang A, Roy D, Pal CJ, Kadoury S (2017) Metastatic liver tumour segmentation with a neural network-guided 3D deformable model. Med Biol Eng Comput 55(1):127–139. https://doi.org/10.1007/s11517-016-1495-8

    Article  Google Scholar 

  67. Masahiro Oda NS, Roth HR, Karasawa K, Kitasaka T, Misawa K, Fujiwara M, Rueckert D, Mori K (2017) 3D FCN feature driven regression forest-based pancreas localization and segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, Québec City, Sep. 10–14. https://doi.org/10.1007/978-3-319-67558-9

  68. Zhu JH, Zhang J, Qiu B, Liu YM, Liu XW, Chen LX (2019) Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques. Acta Oncol 58(2):257–264. https://doi.org/10.1080/0284186x.2018.1529421

    Article  Google Scholar 

  69. Roth H, Oda M, Shimizu N, Oda H, Hayashi Y, Kitasaka T, Fujiwara M, Misawa K, Mori K (2018) Towards dense volumetric pancreas segmentation in CT using 3D fully convolutional networks. Paper presented at the Medical Imaging 2018: Image Processing, Houston, Feb 10–15

  70. Roth HR, Farag A, Lu L, Turkbey EB, Summers RM (2015) Deep convolutional networks for pancreas segmentation in CT imaging. Paper presented at the Medical Imaging 2015: Image Processing, Orlando, Feb 24–26

  71. Zheng S, Jayasumana S, Romera-Paredes B, Vineet V, Su Z, Du D, Huang C, Torr PHS (2015) Conditional random fields as recurrent neural networks. IEEE Int Conf Comput Vis:1529–1537. https://doi.org/10.1109/iccv.2015.179

  72. Li X, Dou Q, Chen H, Fu CW, Qi X, Belavy DL, Armbrecht G, Felsenberg D, Zheng G, Heng PA (2018) 3D multi-scale FCN with random modality voxel dropout learning for intervertebral disc localization and segmentation from multi-modality MR images. Med Image Anal 45:41–54. https://doi.org/10.1016/j.media.2018.01.004

    Article  Google Scholar 

  73. Chen H, Dou Q, Yu L, Heng PA (2016) VoxResNet: deep voxelwise residual networks for volumetric brain segmentation. NeuroImage 170:446–455. https://doi.org/10.1016/j.neuroimage.2017.04.041

    Article  Google Scholar 

  74. Badrinarayanan V, Kendall A, Cipolla R (2017) SegNet: a deep convolutional encoder-decoder architecture for scene segmentation. IEEE Trans Pattern Anal Mach Intell 99:2481–2495

    Article  Google Scholar 

  75. Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. MICCAI, Munich, Oct 5–9. pp 234–241

  76. Zhou Y, Xie L, Shen W, Wang Y, Fishman EK, Yuille AL (2017) A fixed-point model for pancreas segmentation in abdominal CT scans. Paper presented at the MICCAI, Quebec City, Sep 10–14

  77. Yu Q, Xie L, Wang Y, Zhou Y, Fishman EK (2017) Saliency transformation network: incorporating multi-stage visual cues for pancreas segmentation. Conf Comput Vis Pattern Recognit. https://doi.org/10.1109/CVPR.2018.00864

  78. Xie S, Tu Z (2015) Holistically-Nested Edge Detection. IEEE Int Conf Comp Vis:1395–1403. https://doi.org/10.1109/iccv.2015.164

  79. Lee CY, Xie S, Gallagher P, Zhang Z, Tu Z (2014) Deeply-supervised nets. Eprint Arxiv:562–570

  80. Harrison AP, Xu Z, George K, Lu L, Summers RM, Mollura DJ (2017) Progressive and multi-path holistically nested neural networks for pathological lung segmentation from CT images. MICCAI, Quebec City, Sep 10–14. pp 621–629

  81. Roth HR, Lu L, Farag A, Sohn A, Summers RM (2016) Spatial aggregation of holistically-nested networks for automated pancreas segmentation. Paper presented at the MICCAI, Busan, Oct 17–21

  82. Cai J, Lu L, Xie Y, Xing F, Yang L (2017) Improving deep pancreas segmentation in CT and MRI images via recurrent neural contextual learning and direct loss function. Paper presented at the MICCAI, Quebec City, Sep 10–14

  83. Heinrich MP, Oktay O, Heinrich MP, Oktay O (2017) BRIEFnet: deep pancreas segmentation using binary sparse convolutions. Paper presented at the MICCAI, Quebec City, Sep 10–14

  84. Zhu Z, Xia Y, Shen W, Fishman EK, Yuille AL (2017) A 3D coarse-to-fine framework for automatic pancreas segmentation. Paper presented at the 2018 International Conference on 3D Vision, Verona, Sep 5–8

  85. Zhou Y, Xie L, Shen W, Fishman E, Yuille A (2017) Pancreas segmentation in abdominal CT scan: a coarse-to-fine approach. Paper presented at the Information Processing in Medical Imaging 2017, Boone, Jun 25–30

  86. Ma J, Lin F, Wesarg S, Erdt M (2018) A novel Bayesian model incorporating deep neural network and statistical shape model for pancreas segmentation. Paper presented at the MICCAI, Granada, Sep 16–20

  87. Pont-Tuset J, Arbelaez P, Barron JT, Marques F, Malik J (2017) Multiscale combinatorial grouping for image segmentation and object proposal generation. IEEE Trans Pattern Anal 39(1):128–140

    Article  Google Scholar 

  88. Chen X, Pan L (2018) A survey of graph cuts/graph search based medical image segmentation. IEEE Rev Biomed Eng 11:112–124. https://doi.org/10.1109/RBME.2018.2798701

    Article  Google Scholar 

  89. Suzuki T, Takizawa H, Kudo H, Okada T (2016) Interactive segmentation of pancreases from abdominal CT images by use of the graph cut technique with probabilistic atlases. In: Chen YW, Torro C, Tanaka S, Howlett RJ, Jain LC (eds) Innovation in medicine and healthcare 2015, Smart innovation systems and technologies, vol 45, pp 575–584. https://doi.org/10.1007/978-3-319-23024-5_52

    Chapter  Google Scholar 

  90. Dmitriev K, Gutenko I, Nadeem S, Kaufman A (2016) Pancreas and cyst segmentation. Paper presented at the Medical Imaging 2016: Image Processing, San Diego, Feb 27

  91. Makropoulos A, Counsell SJ, Rueckert D (2018) A review on automatic fetal and neonatal brain MRI segmentation. Neuroimage 170:231–248. https://doi.org/10.1016/j.neuroimage.2017.06.074

    Article  Google Scholar 

  92. Dice LR (1945) Measures of the amount of ecologic association between species. Ecology 26(3):297–302

    Article  Google Scholar 

  93. Erdt M, Kirschner M, Drechsler K, Wesarg S, Hammon M, Cavallaro A (2011) Automatic pancreas segmentation in contrast enhanced CT data using learned spatial anatomy and texture descriptors. Int Symp Biomed Imaging:2076–2082. https://doi.org/10.1109/isbi.2011.5872821

  94. Zhou Y, Xie L, Fishman EK, Yuille AL (2017) Deep supervision for pancreatic cyst segmentation in abdominal CT scans. Paper presented at the MICCAI, Quebec City, Sep 10–14

  95. Karasawa K, Kitasaka T, Oda M, Nimura Y, Hayashi Y, Fujiwara M, Misawa K, Rueckert D, Mori K (2016) Structure specific atlas generation and its application to pancreas segmentation from contrasted abdominal CT volumes. Med Comput Vis Algorithms Big Data 9601:47–56. https://doi.org/10.1007/978-3-319-42016-5_5

    Article  Google Scholar 

  96. Karasawa K, Oda M, Kitasaka T, Misawa K, Fujiwara M, Chu C, Zheng G, Rueckert D, Mori K (2017) Multi-atlas pancreas segmentation: atlas selection based on vessel structure. Med Image Anal 39:18–28. https://doi.org/10.1016/j.media.2017.03.006

    Article  Google Scholar 

  97. Cai J, Lu L, Zhang Z, Xing F, Yang L, Yin Q (2016) Pancreas segmentation in MRI using graph-based decision fusion on convolutional neural networks. Med Image Comput Comput Assist Interv 9901:442–450. https://doi.org/10.1007/978-3-319-46723-8_51

    Article  Google Scholar 

  98. Takizawa H, Suzuki T, Kudo H, Okada T (2017) Interactive segmentation of pancreases in abdominal computed tomography images and its evaluation based on segmentation accuracy and interaction costs. Biomed Res Int 2017:5094592. https://doi.org/10.1155/2017/5094592

    Article  Google Scholar 

  99. Okada T, Linguraru MG, Hori M, Summers RM, Tomiyama N, Sato Y (2015) Abdominal multi-organ segmentation from CT images using conditional shape-location and unsupervised intensity priors. Med Image Anal 26(1):1–18. https://doi.org/10.1016/j.media.2015.06.009

    Article  Google Scholar 

  100. Shimizu A, Ohno R, Ikegami T, Kobatake H, Nawano S, Smutek D (2007) Segmentation of multiple organs in non-contrast 3D abdominal CT images. Int J Comput Assist Radiol Surg 2(3–4):135–142. https://doi.org/10.1007/s11548-007-0135-z

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (No.61572239 and No.61772242), Natural Science Foundation Youth Fund (No.61402204) and Postgraduate Innovation Fund of Jiangsu Province (No. KYCX18_2257).

Author information

Authors and Affiliations

  1. School of Computer Science and Telecommunication, Jiangsu University, Zhenjiang, People’s Republic of China

    Xu Yao, Yuqing Song & Zhe Liu

  2. School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, People’s Republic of China

    Xu Yao

Authors
  1. Xu Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuqing Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhe Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xu Yao.

Ethics declarations

Conflict of interest

We declare that this article does not contain any studies with human participants performed by any of the authors, and we have no conflict of interest to this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, X., Song, Y. & Liu, Z. Advances on pancreas segmentation: a review. Multimed Tools Appl 79, 6799–6821 (2020). https://doi.org/10.1007/s11042-019-08320-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-08320-7

Keywords

Search

Navigation