Kidney Segmentation in CT Data Using Hybrid Level-Set Method with Ellipsoidal Shape Constraints

• Autorzy: Skalski A. , Heryan K. , Jakubowski J. , Drewniak T.

Identyfikatory

DOI

10.1515/mms-2017-0006

• Języki publikacji: EN

Abstrakty

EN

With development of medical diagnostic and imaging techniques the sparing surgeries are facilitated. Renal cancer is one of examples. In order to minimize the amount of healthy kidney removed during the treatment procedure, it is essential to design a system that provides three-dimensional visualization prior to the surgery. The information about location of crucial structures (e.g. kidney, renal ureter and arteries) and their mutual spatial arrangement should be delivered to the operator. The introduction of such a system meets both the requirements and expectations of oncological surgeons. In this paper, we present one of the most important steps towards building such a system: a new approach to kidney segmentation from Computed Tomography data. The segmentation is based on the Active Contour Method using the Level Set (LS) framework. During the segmentation process the energy functional describing an image is the subject to minimize. The functional proposed in this paper consists of four terms. In contrast to the original approach containing solely the region and boundary terms, the ellipsoidal shape constraint was also introduced. This additional limitation imposed on evolution of the function prevents from leakage to undesired regions. The proposed methodology was tested on 10 Computed Tomography scans from patients diagnosed with renal cancer. The database contained the results of studies performed in several medical centers and on different devices. The average effectiveness of the proposed solution regarding the Dice Coefficient and average Hausdorff distance was equal to 0.862 and 2.37 mm, respectively. Both the qualitative and quantitative evaluations confirm effectiveness of the proposed solution.

Słowa kluczowe

EN

Level Set method; kidney; CT data; image segmentation; ellipsoid

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2017
• Tom: Vol. 24, nr 1
• Strony: 101--112
• Opis fizyczny: Bibliogr. 26 poz., rys., tab., wykr., wzory

Twórcy

autor

Skalski A.

• AGH University of Science and Technology, Department of Measurement and Electronics, Al. Mickiewicza 30, Cracow, Poland

autor

Heryan K.

• AGH University of Science and Technology, Department of Measurement and Electronics, Al. Mickiewicza 30, Cracow, Poland

autor

Jakubowski J.

• Rydygier Memorial Hospital, Department of Urology, Os. Złotej Jesieni 1, 31-826 Cracow, Poland

autor

Drewniak T.

• Specialized Municipal Hospital G. Narutowicz, Department of Urology, Prądnicka 35-37, 31-202 Cracow, Poland

Bibliografia

• [1] Klatte, T., et al. (2015). A literature review of renal surgical anatomy and surgical strategies for partial nephrectomy. European urology, 68(6), 980-992.
• [2] Shao, P., et al. (2013). Application of a vasculature model and standardization of the renal hilar approach in laparoscopic partial nephrectomy for precise segmental artery clamping. European Urology, 63(6), 1072-1081.
• [3] Bugajska, K., et al. (2015) The renal vessel segmentation for facilitation of partial nephrectomy. IEEE SPA 2015: Signal Processing: Algorithms, Architectures, Arrangements and Applications, 50-55.
• [4] Lin, D.T., Lei, C.C., Hung, S.W. (2006). Computer-aided kidney segmentation on abdominal CT images. IEEE Transactions on Information Technology in Biomedicine, 10(1), 5-65.
• [5] Nedevschi, S., Ciurte, A., Mile, G. (2008). Kidney CT image segmentation using multi-feature EM algorithm, based on Gabor filters. 4th International Conference on Intelligent Computer Communication and Processing, ICCP 2008, 283-286.
• [6] Yang, G., et al. (2014). Automatic kidney segmentation in CT images based on multi-atlas image registration. 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5538-5541.
• [7] Xu, Z., et al. (2015). Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning. Medical image analysis, 24(1), 18-27.
• [8] Chao, J., et al. (2016). 3D Fast Automatic Segmentation of Kidney Based on Modified AAM and Random Forest. Accepted to IEEE Transaction on medical Imaging.
• [9] Spiegel, M., et al. (2009). Segmentation of kidneys using a new active shape model generation technique based on non-rigid image registration. Computerized Medical Imaging and Graphics, 33(1), 29-39.
• [10] Zollner, F.G., Kocinski, M., Rorvik, J. Lundervold. (2007). Towards Automatically Assessment of Kidney Volume from 3D DCE-MRI Time Courses using Active Contours. Proc. Intl. Soc. Mag. Reson. Med., 15.
• [11] Tsagaan, B.S.A., Kobatake, H., Miyakawa, K., Hanzawa, Y. (2001). Segmentation of kidney by using a deformable model. Image Processing, 2001, Proceedings, 2001 International Conference on 3, 1059-1062.
• [12] Khalifa, F., et al. (2011). A new deformable model-based segmentation approach for accurate extraction of the kidney from abdominal CT images. 18th IEEE International Conference on Image Processing (ICIP), 3393-3396.
• [13] Huang, Y., et al. (2009). Multiphase level set with multi dynamic shape models on kidney segmentation of CT image. Biomedical Circuits and Systems Conference, BioCAS, 141-144.
• [14] Chan, T.F., Vese, L.A. (2001). Active contours without edges. IEEE transactions on Image processing, 10(2), 266-277.
• [15] Pluempitiwiriyawej, C., et al. (2005). STACS: New active contour scheme for cardiac MR image segmentation. IEEE Transactions on Medical Imaging, 24(5), 593-603.
• [16] Zhang, Y., et al. (2008). Medical image segmentation using new hybrid level-set method. BioMedical Visualization, 2008, MEDIVIS'08. Fifth International Conference, 71-76.
• [17] Kass, M., Witkin, A., Terzopoulos, D. (1988). Snakes: Active contour models. International journal of computer vision, 1(4), 321-331.
• [18] Brigger, P., Hoeg, J., Unser, M. (2000). B-spline snakes: a flexible tool for parametric contour detection. IEEE Transactions on Image Processing, 9(9), 1484-1496.
• [19] Osher, S., Fedkiw, R. (2006). Level set methods and dynamic implicit surfaces. Springer Science & Business Media.
• [20] Hunyadi, L., Vajk, I. (2014). Constrained quadratic errors-in-variables fitting. The Visual Computer, 30(12), 1347-1358.
• [21] Li, Q., Griffiths, J.G. (2004). Least squares ellipsoid specific fitting. Geometric Modeling and Processing, 335-340.
• [22] McLachlan, G., Peel, D. (2000). Finite Mixture Models. Hoboken. NJ: John Wiley & Sons, Inc.
• [23] Vese, L.A., Chan, T.F. (2002). A multiphase level set framework for image segmentation using the Mumford and Shah model. International journal of computer vision, 50(3), 271-293.
• [24] Yushkevich, P.A., Piven, J., Hazlett, H.C., Smith, R.G., Ho, S., Gee, J.C., Gerig, G. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability. Neuroimage, 31(3), 1116-28.
• [25] Dice, L. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297-302.
• [26] Dubuisson, M.P., et al. (1994). A modified Hausdorff distance for object matching. ICPR94, 566-68.

Uwagi

EN

The work was supported by the Ministry of Science and Higher Education, Poland (Dean Grants, statutory activity).

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).