Automated segmentation of the injured kidney due to abdominal trauma

  • Gokalp TulumEmail author
  • Uygar Teomete
  • Ferhat Cuce
  • Tuncer Ergin
  • Murathan Koksal
  • Ozgur Dandin
  • Onur Osman
Image & Signal Processing
Part of the following topical collections:
  1. Image & Signal Processing


The objective of this study is to propose and validate a computer-aided segmentation system which performs the automated segmentation of injured kidney in the presence of contusion, peri-, intra-, sub-capsular hematoma, laceration, active extravasation and urine leak due to abdominal trauma. In the present study, total multi-phase CT scans of thirty-seven cases were used; seventeen of them for the development of the method and twenty of them for the validation of the method. The proposed algorithm contains three steps: determination of the kidney mask using Circular Hough Transform, segmentation of the renal parenchyma of the kidney applying the symmetry property to the histogram, and estimation of the kidney volume. The results of the proposed method were compared using various metrics. The kidney quantification led to 92.3 ± 4.2% Dice coefficient, 92.8 ± 7.4%/92.3 ± 5.1% precision/sensitivity, 1.4 ± 0.6 mm/2.0 ± 1.0 mm average surface distance/root-mean-squared error for intact and 87.3 ± 8.4% Dice coefficient, 84.3 ± 13.8%/92.2 ± 3.8% precision/sensitivity and 2.4 ± 2.2 mm/4.0 ± 4.2 mm average surface distance/root-mean-squared error for injured kidneys. The segmentation of the injured kidney was satisfactorily performed in all cases. This method may lead to the automated detection of renal lesions due to abdominal trauma and estimate the intraperitoneal blood amount, which is vital for trauma patients.


Abdominal trauma Solid organ injuries Injured kidney Automated segmentation 


Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflicts of interest. No funding was received for this study.

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed Consent

Informed consent was obtained from all individual participants included in the study.


  1. 1.
    Nishijima, D. K., Simel, D. L., Wisner, D. H. et al., Does this adult patient have a blunt intra-abdominal injury. JAMA 307:1517–1527, 2012.CrossRefGoogle Scholar
  2. 2.
    Linguraru, M. G., Pura, J. A., Chowdhury, A. S. et al., Multi-Organ Segmentation from Multi-Phase Abdominal CT via 4D Graphs using Enhancement, Shape, and Location Optimization. Med. Image Comput. Assist. Interv. 13:89–96, 2010.Google Scholar
  3. 3.
    Linguraru, M. G., Pura, J. A., Pamulapati, V. et al., Statistical4D graphs for multi-organ abdominal segmentation from multiphase CT. Med. Image Anal. 16:904–914, 2012.CrossRefGoogle Scholar
  4. 4.
    Li, C., Wang, X., Li, J. et al., Joint probabilistic model of shape and intensity for multiple abdominal organ segmentation from volumetric CT images. IEEE J. Biomed. Health Inf. 17:92–102, 2013.CrossRefGoogle Scholar
  5. 5.
    Wolz, R., Chu, C., Misawa, K. et al., Automated abdominal multi-organ segmentation with subject-specific atlas generation. IEEE Trans. Med. Imaging 32:1723–1730, 2013.CrossRefGoogle Scholar
  6. 6.
    Badakhshannoory, H., and Saeedi, P., A model-based validation scheme for organ segmentation in CT scan volumes. IEEE Trans. Biomed. Eng. 58:2681–2693, 2011.CrossRefGoogle Scholar
  7. 7.
    Chen, X., Summers, R. M., Cho, M. et al., An automatic method for renal cortex segmentation on CT images: evaluation on kidney donors. Acad. Radiol. 19(5):562–570, 2012.CrossRefGoogle Scholar
  8. 8.
    Muto, N. S., Kamishima, T., Harris, A. A. et al., Renal cortical volume measured using automatic contouring software for computed tomography and its relationship with BMI, age, and renal function. Eur. J. Radiol. 78:151–156, 2011.CrossRefGoogle Scholar
  9. 9.
    Torimoto, I., Takebayashi, S., Sekikawa, Z. et al., Renal Perfusional Cortex Volume for Arterial Input Function Measured by Semiautomatic Segmentation Technique Using MDCT Angiographic Data with 0.5-mm Collimation. Am. J. Roentgenol. 204(1):98–104, 2015.CrossRefGoogle Scholar
  10. 10.
    Li, S., Jiang, H., Yao, Y., and Yang, B., Organ Location Determination and Contour Sparse Representation for Multiorgan Segmentation. IEEE J. Biomed. Health Inf. 22:852–861, 2018.CrossRefGoogle Scholar
  11. 11.
    Gibson, E., Giganti, F., Hu, Y. et al., Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks. IEEE Trans. Med. Imaging 37:1822–1834, 2018. Scholar
  12. 12.
    Khalifa F, Soliman A, Elmaghraby A, Gimel’farb G, El-Baz A (2017) 3D Kidney Segmentation from Abdominal Images Using Spatial-Appearance Models. Comput. Math Methods Med. Scholar
  13. 13.
    Davis, M. L., Stitzel, J. D., and Gayzik, F. S., Thoracoabdominal organ volumes for small women. Traffic Injury Prevent. 16:611–617, 2015.CrossRefGoogle Scholar
  14. 14.
    Rezai, P., Tochetto, S., Galizia, M. et al., Perinephric hematoma: semi-automated quantification of volume on MDCT: a feasibility study. Abdom. Imaging 36:222–227, 2011.CrossRefGoogle Scholar
  15. 15.
    Kanki, A., Ito, K., Tamada, T. et al., Dynamic Contrast-Enhanced CT of Abdomen to Predict Clinical Prognosis in Patients with Hypovolemic Shock. Am. J. Roentgenol. 197:980–984, 2011.CrossRefGoogle Scholar
  16. 16.
    Kiw-Yong, K., Young Joon, L., Soon Chul, P. et al., A Comparative Study of Methods of Estimating Kidney Length in Kidney Transplantation Donors. Nephrol. Dial. Transplant. 22:2322–2327, 2007.CrossRefGoogle Scholar
  17. 17.
    Illingworth, J., and Kittler, J., The Adaptive Hough Transform. IEEE Trans. Pattern Anal. Mach. Intell. 9:690–698, 1987.CrossRefGoogle Scholar
  18. 18.
    Mao, S. S., Ahmadi, N., Shah, B. et al., Normal Thoracic Aorta Diameter on Cardia Computed Tomography in Healthy Asymptomatic Adults: Impact of Age and Gender. Acad. Radiol. 15:827–834, 2008.CrossRefGoogle Scholar
  19. 19.
    Farzaneh, N., Reza Soroushmehr, S.M., Patel, H., et al. Automated Kidney Segmentation for Traumatic Injured Patients through Ensemble Learning and Active Contour Modeling. Conf. Proc. IEEE Eng. Med. Biol. Soc. 3418–3421, 2018.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Gokalp Tulum
    • 1
    Email author
  • Uygar Teomete
    • 2
  • Ferhat Cuce
    • 3
  • Tuncer Ergin
    • 3
  • Murathan Koksal
    • 4
  • Ozgur Dandin
    • 5
  • Onur Osman
    • 1
  1. 1.Department of Electrical and Electronics EngineeringIstanbul Arel UniversityIstanbulTurkey
  2. 2.Department of RadiologySparrow Health SystemLansingUSA
  3. 3.Department of RadiologyGulhane Research and Training HospitalAnkaraTurkey
  4. 4.Department of RadiologyAnkara Numune Training and Research HospitalAnkaraTurkey
  5. 5.Department of General SurgeryAkdeniz UniversityAntalyaTurkey

Personalised recommendations