Abstract
Uterine fibroids are benign tumors that can affect female patients during reproductive years. Magnetic resonance-guided focused ultrasound (MRgFUS) represents a noninvasive approach that uses thermal ablation principles to treat symptomatic fibroids. During traditional treatment planning, uterus, fibroids, and surrounding organs at risk must be manually marked on MR images by an operator. After treatment, an operator must segment, again manually, treated areas to evaluate the non-perfused volume (NPV) inside the fibroids. Both pre- and post-treatment procedures are time-consuming and operator-dependent. This paper presents a novel method, based on an advanced direct region detection model, for fibroid segmentation in MR images to address MRgFUS post-treatment segmentation issues. An incremental procedure is proposed: split-and-merge algorithm results are employed as multiple seed-region selections by an adaptive region growing procedure. The proposed approach segments multiple fibroids with different pixel intensity, even in the same MR image. The method was evaluated using area-based and distance-based metrics and was compared with other similar works in the literature. Segmentation results, performed on 14 patients, demonstrated the effectiveness of the proposed approach showing a sensitivity of 84.05 %, a specificity of 92.84 %, and a speedup factor of 1.56× with respect to classic region growing implementations (average values).
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References
Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal 16(6):641–647. doi:10.1109/34.295913
Ben-Zadok N, Riklin-Raviv T, Kiryati N (2009) Interactive level set segmentation for image-guided therapy. Biomed Imaging Nano Macro, ISBI ‘09. pp 1079–1082, 28 June 2009–1 July 2009. doi:10.1109/ISBI.2009.5193243
Chang Y-L, Li X (1994) Adaptive image region-growing. IEEE Trans Image Process 3(6):868–872. doi:10.1109/83.336259
Chapman A, ter Haar G (2007) Thermal ablation of uterine fibroids using MR-guided focused ultrasound—a truly non-invasive treatment modality. Eur Radiol 17(10):2505–2511. doi:10.1007/s00330-007-0644-8
Di Stefano L, Bulgarelli Andrea (1999) A simple and efficient connected components labeling algorithm. Image Anal Process. In: Proceedings of international conference on 1999, pp 322–327. doi:10.1109/ICIAP.1999.797615
Fallahi A, Pooyan M, Hashemi H, Ghanaati H, Oghabian MA, Khotanlou H, Shakiba M, Jalali AH, Firouznia K (2011) Uterine segmentation and volume measurement in uterine fibroid patients MRI using fuzzy c-mean algorithm and morphological operations. Iran J Radiol 8(3):150–156. doi:10.5812/kmp.iranjradiol.17351065.3142
Fallahi A, Pooyan M, Khotanlou H, Hashemi H, Firouznia K, Oghabian MA (2010) Uterine fibroid segmentation on multiplan MRI using FCM, MPFCM and morphological operations. Computer Engineering and Technology (ICCET), In: 2nd International Conference on, 7:V7-1, V7-5, 16–18 April 2010. doi:10.1109/ICCET.2010.5485920
Faruquzzaman ABM, Paiker NR, Arafat J, Karim Z, Ameer Ali M (2008) Object segmentation based on split and merge algorithm. In: TENCON 2008-2008 IEEE region 10 conference, vol 1, pp 19–21. doi:10.1109/TENCON.2008.4766802
Fenster A, Chiu B (2005) Evaluation of segmentation algorithms for medical imaging. Eng Med Biol Soc. In: IEEE-EMBS 2005. 27th annual international conference of the IEEE, pp 7186–7189. doi: 10.1109/IEMBS.2005.1616166
Ferrari RJ (2013) Off-line determination of the optimal number of iterations of the robust anisotropic diffusion filter applied to denoising of brain MR images. Med Biol Eng Comput 51(1–2):71–88. doi:10.1007/s11517-012-0971-z
Gambino O, Vitabile S, Lo Re G, La Tona G, Librizzi S, Pirrone R, Ardizzone E, Midiri M (2010) Automatic volumetric liver segmentation using texture based region growing. Proc Inter Conf Complex Intell Softw Intensive Syst 2010:146–152. doi:10.1109/CISIS.2010.118
Gonzalez RC, Woods RE (2007) Digital image processing, 3rd edn. Prentice Hall, Englewood Cliffs. ISBN: 013168728X
Hesley GK, Felmlee JP, Gebhart JB, Dunagan KT, Gorny KR, Kesler JB, Brandt KR, Glantz JN, Gostout BS (2006) Noninvasive treatment of uterine fibroids: early Mayo Clinic experience with magnetic resonance imaging-guided focused ultrasound. Mayo Clin Proc 81(7):936–942. doi:10.4065/81.7.936
Horowitz SL, Pavlidis T (1976) Picture segmentation by a tree transversal algorithm. J ACM 23:368–388. doi:10.1145/321941.321956
Kamdi S, Krishna RK (2012) Image segmentation and region growing algorithm. Int J Comput Technol Electron Eng (IJCTEE), vol 2. ISSN: 2249-6343
Liu H-T, Sheu TWH, Chang H-H (2013) Automatic segmentation of brain MR images using an adaptive balloon snake model with fuzzy classification. Med Biol Eng Comput 51(10):1091–1104. doi:10.1007/s11517-013-1089-7
Manousakas IN, Undrill PE, Cameron GG, Redpath TW (1998) Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. Comput Biomed Res 31(6):393–412. doi:10.1006/cbmr.1998.1489
Militello C, Rundo L, Gilardi MC (2014) Applications of imaging processing to MRgFUS treatment for fibroids: a review. Transl Cancer Res 3(5):472–482. doi:10.3978/j.issn.2218-676X.2014.09.06
Militello C, Vitabile S, Russo G, Candiano G, Gagliardo C, Midiri M, Gilardi MC (2013) A semi-automatic multi-seed region-growing approach for uterine fibroids segmentation in MRgFUS treatment. In: Proceedings—2013 7th international conference on complex, intelligent, and software intensive systems, CISIS 2013, art.no.6603885, pp 176–182. doi:10.1109/CISIS.2013.36
Otsu N (1979) A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9(1):62–66. doi:10.1109/TSMC.1979.4310076
Pohle R, Toennies KD (2001) Segmentation of medical images using adaptive region growing. In: Proceedings of SPIE 4322, medical imaging 2001: image processing, 1337 (July 3 2001). doi:10.1117/12.431013
Roberts A (2008) Magnetic resonance-guided focused ultrasound for uterine fibroids. Semin Interv Radiol 25(4):394–405. doi:10.1055/s-0028-1102999
Ryan GL, Syrop CH, Van Voorhis BJ (2005) Role, epidemiology, and natural history of benign uterine mass lesions. Clin Obstet Gynecol 48(2):312–324. doi:10.1097/01.grf.0000159538.27221.8c
Saad NM, Abu-Bakar SAR, Muda S, Mokji M (2010) Automated segmentation of brain lesion based on diffusion-weighted MRI using a split and merge approach. In: Biomedical engineering and sciences (IECBES), 2010 IEEE EMBS conference on, 30 Nov 2010–2 Dec 2010, pp 475–480. doi:10.1109/IECBES.2010.5742284
Saad NM, Abu-Bakar SAR, Muda S, Mokji M, Abdullah AR (2012) Automated region growing for segmentation of brain lesion in diffusion-weighted MRI. In: Proceedings of the international multi conference of engineers and computer scientists, IMECS 2012, vol 1, pp 674–677. ISSN: 2078-0958
Sasidharan A, Malarkhodi S (2010) Segmentation and Volume Measurement of Uterine Fibroid on MRI Images. Int J Adv Eng Appl 3(3):20–26. ISSN: 2321-7723
Sijbers J, Scheunders P, Verhoye M, Van der Linden A, Van Dyck D, Raman E (1997) Watershed-based segmentation of 3D MR data for volume quantization. Magn Reson Imaging 15(6):679–688. doi:10.1016/S0730-725X(97)00033-7
Soille P (2003) Morphological image analysis: principles and applications, 2nd edn. Springer, New York. ISBN: 3540429883
Soille P, Talbot H (2001) Directional morphological filtering. IEEE Trans Pattern Anal 23(11):1313–1329. doi:10.1109/34.969120
Stewart EA, Gostout B, Rabinovici J, Kim HS, Regan L, Tempany CM (2007) Sustained relief of leiomyoma symptoms by using focused ultrasound surgery. Obstet Gynecol 110(2 Pt 1):279–287. doi:10.1097/01.AOG.0000275283.39475.f6
Sun C (2006) Moving average algorithms for diamond, hexagon, and general polygonal shaped window operations. Pattern Recogn Lett 27(6):556–566. doi:10.1016/j.patrec.2005.09.020
Verkauf BS (1993) Changing trends in treatment of leiomyomata uteri. Curr Opin Obstet Gynecol 5(3):301–310
Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE T Pattern Anal 13(6):583–598. doi:10.1109/34.87344
Yao J, Chen D, Lu W, Premkumar A (2006) Uterine fibroid segmentation and volume measurement on MRI. Medical Imaging 2006: physiology, function, and structure from medical images. In: Manduca A, Amini Amir A (ed). Proceedings of the SPIE vol 6143, pp 640–649. doi:10.1117/12.653856
Yin XX, Ng BW-H, Yang Q, Pitman A, Ramamohanarao K, Abbott D (2012) Anatomical landmark localization in breast dynamic contrast-enhanced MR imaging. Med Biol Eng Comput 50(1):91–101. doi:10.1007/s11517-011-0772-9
Zhang Y (1996) A survey on evaluation methods for image segmentation. Pattern Recogn 29:1335–1346. doi:10.1016/0031-3203(95)00169-7
Acknowledgments
This work was supported by “Development of a New Technological Platform Based on Focused Ultrasounds for Non Invasive Treatment of Tumors and Infections” project (PON 01_01059) and by “Smart Health 2.0” MIUR project (PON 04a2_C).
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Rundo, L., Militello, C., Vitabile, S. et al. Combining split-and-merge and multi-seed region growing algorithms for uterine fibroid segmentation in MRgFUS treatments. Med Biol Eng Comput 54, 1071–1084 (2016). https://doi.org/10.1007/s11517-015-1404-6
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DOI: https://doi.org/10.1007/s11517-015-1404-6