Abstract
Automatic segmentation of anatomical structures in CT scans is an essential step in the analysis of radiological patient data and is a prerequisite for large-scale content-based image retrieval (CBIR). Many existing segmentation methods are tailored to a single structure and/or require an atlas, which entails multistructure deformable registration and is time-consuming. We present a fully automatic atlas-free segmentation of multiple organs of the ventral cavity in contrast-enhanced CT scans of the whole trunk (CECT). Our method uses a pipeline approach based on the rules that determine the order in which the organs are isolated and how they are segmented. Each organ is individually segmented with a generic four-step procedure. Our method is unique in that it does not require any predefined atlas or a costly registration step and in that it uses the same generic segmentation approach for all organs. Experimental results on the segmentation of seven organs—liver, left and right kidneys, left and right lungs, trachea, and spleen—on 20 CECT scans of the VISCERAL Anatomy training dataset and 10 CECT scans of the test dataset yield an average DICE volume overlap similarity score of 90.95 and 88.50%, respectively.
Chapter PDF
Similar content being viewed by others
Keywords
- Statistical Shape Model
- Breathing System
- Deformable Registration
- Spectral Cluster Algorithm
- Coarse Segmentation
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
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
Boykov Y, Funka-Lea G (2006) Graph cuts and efficient N-D image segmentation. Int J Comput Vis 70(2):109–131
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79
Deserno TM, Antani S, Long R (2009) Ontology of gaps in content-based image retrieval. J Digit Imaging 22(2):202–215
Freiman M, Eliassaf O, Taieb Y, Joskowicz L, Azraq Y, Sosna J (2008) An iterative bayesian approach for nearly automatic liver segmentation: algorithm and validation. Int J Comput Assist Radiol Surg 3(5):439–446
Freiman M, Kronman A, Esses SJ, Joskowicz L, Sosna J (2010) Non-parametric iterative model constraint graph min-cut for automatic kidney segmentation. Med Image Comput Comput Assist Interv, 13:73–80
Goksel O, Gass T, Szekely G (2014) Segmentation and landmark localization based on multiple atlases. In: CEUR workshop proceedings, pp 37–43
Gudewar AD, Ragha LR (2012) Ontology to improve CBIR system. Int J Comput Appl 52(21):23–30
Haris K, Efstratiadis SN, Maglaveras N, Katsaggelos AK (1998) Hybrid image segmentation using watershed and fast region merging. IEEE Trans Image Process 7(12):1684–1699
Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563
Hwang KH, Lee H, Choi D (2012) Medical image retrieval: past and present. Healthc Inf Res 18(1):3–9
Jiménez del Toro ÓA, Müller H (2014) Multi-structure atlas-based segmentation using anatomical regions of interest. In: Menze B, Langs G, Montillo A, Kelm M, Müller H, Tu Z (eds) MCV 2013. LNCS, vol 8331. Springer, Cham, pp 217–221. doi:10.1007/978-3-319-05530-5_21
Jiménez del Toro OA, Goksel O, Menze B, Müller H, Langs G, Weber MA, Eggel I, Gruenberg K, Holzer M, Jakab A, Kotsios-Kontokotsios G, Krenn M, Fernandez TS, Schaer R, Taha AA, Winterstein M, Hanbury A (2014) VISCERAL—VISual concept extraction challenge in RAdioLogy: ISBI 2014 challenge organization. In: Goksel O (ed) Proceedings of the VISCERAL challenge at ISBI, Beijing, China, no. 1194 in CEUR workshop proceedings, pp 6–15. http://ceur-ws.org/Vol-1194/visceralISBI14-0.pdf
Jiménez del Toro OA, Müller H, Krenn M, Gruenberg K, Taha AA, Winterstein M, Eggel I, Foncubierta-Rodriguez A, Goksel O, Jakab A, Kontokotsios G, Langs G, Menze B, Fernandez TS, Schaer R, Walleyo A, Weber MA, Cid YD, Gass T, Heinrich M, Jia F, Kahl F, Kechichian R, Mai D, Spanier A, Vincent G, Wang C, Wyeth D, Hanbury A (2016) Cloud-based evaluation of anatomical structure segmentation and landmark detection algorithms: visceral anatomy benchmarks. IEEE Trans Med Imaging 99:1–1. doi:10.1109/TMI.2016.2578680
Kéchichian R, Valette S, Sdika M, Desvignes M (2014) Automatic 3D multiorgan segmentation via clustering and graph cut using spatial relations and hierarchically-registered atlases. In: Menze B, Langs G, Montillo A, Kelm M, Müller H, Zhang S, Cai WT, Metaxas D (eds) MCV 2014. LNCS, vol 8848. Springer, Cham, pp 201–209. doi:10.1007/978-3-319-13972-2_19
Kronman A, Joskowicz L, Sosna J (2012) Anatomical structures segmentation by spherical 3D ray casting and gradient domain editing. In: Ayache N, Delingette H, Golland P, Mori K (eds) MICCAI 2012. LNCS, vol 7511. Springer, Heidelberg, pp 363–370. doi:10.1007/978-3-642-33418-4_45
Langs G, Hanbury A, Menze B, Müller H (2013) VISCERAL: towards large data in medical imaging — challenges and directions. In: Greenspan H, Müller H, Syeda-Mahmood T (eds) MCBR-CDS 2012. LNCS, vol 7723. Springer, Heidelberg, pp 92–98. doi:10.1007/978-3-642-36678-9_9
Mharib AM, Rahman A, Mashohor S, Binti R (2012) Survey on liver CT image segmentation methods. Artif Intell Rev 37(2):83–95
Müller H, Zhou X, Depeursinge A, Pitkanen M, Iavindrasana J, Geissbuhler A (2007) Medical visual information retrieval: state of the art and challenges ahead. In: IEEE international conference on multimedia and expo. IEEE, pp 683–686
Ng AY, Jordan MI, Weiss Y (2002) On spectral clustering: analysis and an algorithm. Adv Neural Inf Process Syst 2:849–856
Okada T, Yokota K, Hori M, Nakamoto M, Nakamura H, Sato Y (2008) Construction of hierarchical multi-organ statistical atlases and their application to multi-organ segmentation from CT images. In: Metaxas D, Axel L, Fichtinger G, Székely G (eds) MICCAI 2008. LNCS, vol 5241. Springer, Heidelberg, pp 502–509. doi:10.1007/978-3-540-85988-8_60
Pham DL, Xu C, Prince JL (2000) Current methods in medical image segmentation. Annu Rev Biomed Eng 2(1):315–337
Rohlfing T, Brandt R, Menzel R, Russakoff DB, Maurer CR (2005) Quo vadis, atlas-based segmentation? Springer, Boston
Rubin DL (2011) Informatics in radiology: measuring and improving quality in radiology: meeting the challenge with informatics. Radiographics 31(6):1511–1527
Rubin DL (2012) Finding the meaning in images: annotation and image markup (maintained). Philos Psychiatry Psychol 18(4):311–318
Schmidt G, Athelogou M (2007) Cognition network technology for a fully automated 3D segmentation of liver. In: Proceedings of the MICCAI workshop 3-D segmentation clinic: a grand, challenge, pp 125–133
Simonyan K, Zisserman A, Criminisi A (2011) Immediate structured visual search for medical images. In: Fichtinger G, Martel A, Peters T (eds) MICCAI 2011. LNCS, vol 6893. Springer, Heidelberg, pp 288–296. doi:10.1007/978-3-642-23626-6_36
Sluimer I, Schilham A, Prokop M, van Ginneken B (2006) Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging 25:385–405
Tong T, Wolz R, Wang Z, Gao Q, Misawa K, Fujiwara M, Mori K, Hajnal JV, Rueckert D (2015) Discriminative dictionary learning for abdominal multi-organ segmentation. Med Image Anal 23(1):92–104
Tsai A, Yezzi A Jr, Wells W, Tempany C, Tucker D, Fan A, Grimson WE, Willsky A (2003) A shape-based approach to the segmentation of medical imagery using level sets. Med Imaging 22(2):137–154
Valente F, Costa C, Silva A (2013) Content based retrieval systems in a clinical context, chap 1. In: Felix Erondu O (ed) Medical imaging in clinical practice. InTech, Rijeka
Wang C, Smedby O (2014) Automatic multi-organ segmentation in non-enhanced CT datasets using hierarchical shape priors. In: 22nd international conference on pattern recognition (ICPR), pp 3327–3332
Wolz R, Chu C, Misawa K, Mori K, Rueckert D (2012) Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases. In: Ayache N, Delingette H, Golland P, Mori K (eds) MICCAI 2012. LNCS, vol 7510. Springer, Heidelberg, pp 10–17. doi:10.1007/978-3-642-33415-3_2
Li X, Huang C, Jia F, Li Z, Fang C, Fan Y (2014) Automatic liver segmentation using statistical prior models and free-form deformation. In: Menze B, Langs G, Montillo A, Kelm M, Müller H, Zhang S, Cai WT, Metaxas D (eds) MCV 2014. LNCS, vol 8848. Springer, Cham, pp 181–188. doi:10.1007/978-3-319-13972-2_17
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, duplication, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the work’s Creative Commons license, unless indicated otherwise in the credit line; if such material is not included in the work’s Creative Commons license and the respective action is not permitted by statutory regulation, users will need to obtain permission from the license holder to duplicate, adapt or reproduce the material.
Copyright information
© 2017 The Author(s)
About this chapter
Cite this chapter
Spanier, A.B., Joskowicz, L. (2017). Automatic Atlas-Free Multiorgan Segmentation of Contrast-Enhanced CT Scans. In: Hanbury, A., MĂĽller, H., Langs, G. (eds) Cloud-Based Benchmarking of Medical Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-49644-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-49644-3_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49642-9
Online ISBN: 978-3-319-49644-3
eBook Packages: Computer ScienceComputer Science (R0)