Abstract
In this chapter, we introduce an automatic multiorgan segmentation method using a hierarchical-shape-prior-guided level set method. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that the children structures are always contained by the parent structure. This hierarchical approach solves two challenges of multiorgan segmentation. First, it gradually refines the prediction of the organs’ position by locating and segmenting the larger parent structure. Second, it solves the ambiguity of boundary between two attaching organs by looking at a large scale and imposing the additional shape constraint of the higher-level structures. To improve the segmentation accuracy, a model-guided local phase term is introduced and integrated with the conventional region-based energy function to guide the level set propagation. Finally, a novel coherent propagation method is implemented to speed up the model-based level set segmentation. In the VISCERAL Anatomy challenge, the proposed method delivered promising results on a number of abdominal organs.
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Belaid A, Boukerroui D, Maingourd Y, Lerallut JF (2011) Phase-based level set segmentation of ultrasound images. IEEE Trans Inf Technol Biomed 15(1):138–147. doi:10.1109/TITB.2010.2090889
Caselles V, Kimmel R, Sapiro G (1997) Geodesic active contours. Int J Comput Vis 22(1):61–79. doi:10.1023/A:1007979827043
Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10(2):266–277. doi:10.1109/83.902291
Cootes T, Taylor C, Cooper D, Graham J (1995) Active shape models-their training and application. Comput Vis Image Underst 61(1):38–59. doi:10.1006/cviu.1995.1004
Cremers D, Rousson M, Deriche R (2007) A review of statistical approaches to level set segmentation: integrating color, texture, motion and shape. Int J Comput Vis 72(2):195–215. doi:10.1007/s11263-006-8711-1
Freeman WT, Adelson EH (1991) The design and use of steerable filters. IEEE Trans Pattern Anal Mach Intell 13(9):891–906. doi:10.1109/34.93808
Heimann T, Meinzer HP (2009) Statistical shape models for 3D medical image segmentation: a review. Med Image Anal 13(4):543–563. doi:10.1016/j.media.2009.05.004
Jiménez del Toro OJ, 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. doi:10.1109/TMI.2016.2578680
Kainmueller D, Lange T, Lamecker H (2007) Shape constrained automatic segmentation of the liver based on a heuristic intensity model. In: Proceedings of MICCAI workshop on 3D segmentation in the clinic: a grand challenge, pp 109–116
Knutsson H (1994) Signal processing for computer vision. Springer, Berlin
Läthén G, Jonasson J, Borga M (2010) Blood vessel segmentation using multi-scale quadrature filtering. Pattern Recognit Lett 31(8):762–767. doi:10.1016/j.patrec.2009.09.020
Lefohn AE, Cates JE, Whitaker RT (2003) Interactive, GPU-based level sets for 3D segmentation. In: Ellis RE, Peters TM (eds) Proceedings of the 6th international conference medical image computing and computer-assisted intervention—MICCAI, Montréal, Canada, Nov 15–18, 2003. Springer, Berlin, pp 564–572. doi:10.1007/978-3-540-39899-8_70
Leventon ME, Grimson WEL, Faugeras O (2000) Statistical shape influence in geodesic active contours. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2000, vol 1, pp 316–323. doi:10.1109/CVPR.2000.855835
Wang C, Lundström C (2016) CT scan range estimation using multiple body parts detection: let PACS learn the CT image content. Int J Comput Assist Radiol Surg 11(2):317–325. doi:10.1007/s11548-015-1232-z
Wang C, Smedby Ö (2013) Fully automatic brain segmentation using model-guided level sets and skeleton-based models. MIDAS J
Wang C, Smedby Ö (2014) Automatic multi-organ segmentation using fast model based level set method and hierarchical shape priors. In: Goksel O (ed) Proceedings of the VISCERAL challenge at ISBI, Beijing, China, no. 1194 in CEUR workshop proceedings, pp 25–31. http://ceur-ws.org/Vol-1194/visceralISBI14-0.pdf
Wang C, Smedby Ö (2014) Automatic multi-organ segmentation in non-enhanced CT datasets using hierarchical shape priors. In: 2014 22nd international conference on pattern recognition (ICPR), pp 3327–3332. doi:10.1109/ICPR.2014.574
Wang C, Smedby Ö (2014) Model-based left ventricle segmentation in 3D ultrasound using phase image. MIDAS J
Wang C, Smedby Ö (2015) Multi-organ segmentation using shape model guided local phase analysis. Springer, Berlin, pp 149–156. doi:10.1007/978-3-319-24574-4_18
Wang C, Frimmel H, Smedby O (2011) Level-set based vessel segmentation accelerated with periodic monotonic speed function. In: Proceedings of the SPIE medical imaging conference, p 79621M. doi:10.1117/12.876704
Wang C, Moreno R, Smedby Ö (2012) Vessel segmentation using implicit model-guided level sets. In: Proceedings of the 3D cardiovascular imaging: a MICCAI segmentation challenge workshop
Wang C, Frimmel H, Smedby O (2014) Fast level-set based image segmentation using coherent propagation. Med Phys 41(7):073501. doi:10.1118/1.4881315
Wang C, Dahlström N, Fransson SG, Lundström C, Smedby Ö (2015) Real-time interactive 3D tumor segmentation using a fast level-set algorithm. J Med Imaging Health Inf 5(8):1998–2002. doi:10.1166/jmihi.2015.1685
Whitaker RT (1998) A level-set approach to 3D reconstruction from range data. Int J Comput Vis 29(3):203–231. doi:10.1023/A:1008036829907
Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D (2008) Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features. IEEE Trans Med Imaging 27(11):1668–1681. doi:10.1109/TMI.2008.2004421
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Wang, C., Smedby, Ö. (2017). Multiorgan Segmentation Using Coherent Propagating Level Set Method Guided by Hierarchical Shape Priors and Local Phase Information. In: Hanbury, A., Müller, H., Langs, G. (eds) Cloud-Based Benchmarking of Medical Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-49644-3_10
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