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Computer-assisted delineation of hematoma from CT volume using autoencoder and Chan Vese model

  • Manas Kumar Nag
  • Saunak Chatterjee
  • Anup Kumar Sadhu
  • Jyotirmoy Chatterjee
  • Nirmalya GhoshEmail author
Original Article
  • 100 Downloads

Abstract

Purpose

To reduce the inter- and intra- rater variability as well as time and effort, a method for computer-assisted delineation of hematoma is proposed. Delineation of hematoma is done for further automated analysis such as the volume of hematoma, anatomical location of hematoma, etc. for proper surgical planning.

Methods

Fuzzy-based intensifier was used as a pre-processing technique for enhancing the computed tomography (CT) volume. Autoencoder was trained to detect the CT slices with hematoma for initialization. Then active contour Chan–Vese model was used for automated delineation of hematoma from CT volume.

Results

The proposed algorithm was tested on 48 hemorrhagic patients. Two radiologists have independently segmented the hematoma manually from CT volume. The intersection of two volumes was used as ground-truth for comparison with the segmentation performed by the proposed method. The accuracy was determined by using similarity matrices. The result of sensitivity, positive predictive value, Jaccard index and Dice similarity index were calculated as 0.71 ± 0.12, 0.73 ± 0.18, 0.55 ± 0.14, and 0.70 ± 0.12 respectively.

Conclusions

A new approach for delineation of hematoma is proposed. The algorithm works well with the whole volume. Similarity indices of the proposed method are comparable with the existing state of art.

Keywords

Hematoma Computed tomography Autoencoders Chan–Vese model 

Notes

Acknowledgements

The first author would like to acknowledge Council of Scientific and Industrial Research (CSIR) Senior Research Fellowship grant (No: 9/81(1296)/17) for financial support.

Compliance with the ethical standard

Conflict of interest

The authors declare that they do not have any conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with ethical standard 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 the participants included in this study.

References

  1. 1.
    Pustina D, Coslett HB, Turkeltaub PE, Tustison N, Schwartz M, Avants B (2016) Automated segmentation of chronic stroke lesions using LINDA: lesion identification with neighborhood data analysis. Hum Brain Mapp 37:1405–1421.  https://doi.org/10.1002/hbm.23110 CrossRefGoogle Scholar
  2. 2.
    Cheng D-C, Cheng K-S (1998) A PC-based medical image analysis system for brain CT hemorrhage area extraction. In: 11th IEEE symposium on computer-based medical systems. Proceedings. IEEE, pp 240–245Google Scholar
  3. 3.
    Loncaric S, Dhawan AP, Cosic D, Damagoj K, Joseph B, Thomas B (1999) Quantitative intracerebral brain hemorrhage analysis. In: Medical imaging 1999: image processing. international society for optics and photonics, pp 886–895Google Scholar
  4. 4.
    Chan T (2007) Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Imaging Graph 31:285–298CrossRefGoogle Scholar
  5. 5.
    Anbeek P, Vincken KL, van Osch MJP, Robertus HCB, Jeroen VG (2004) Automatic segmentation of different-sized white matter lesions by voxel probability estimation. Med Image Anal 8:205–215.  https://doi.org/10.1016/j.media.2004.06.019 CrossRefGoogle Scholar
  6. 6.
    Seghier ML, Ramlackhansingh A, Crinion J, Leff AP, Price CJ (2008) Lesion identification using unified segmentation-normalisation models and fuzzy clustering. Neuroimage 41:1253–1266CrossRefGoogle Scholar
  7. 7.
    Liao CC, Xiao F, Wong JM, Chiang IJ (2009) A multiresolution binary level set method and its application to intracranial hematoma segmentation. Comput Med Imaging Graph 33:423–430.  https://doi.org/10.1016/j.compmedimag.2009.04.001 CrossRefGoogle Scholar
  8. 8.
    Bardera A, Boada I, Feixas M, Romello S, Blasco G, Silva Y, Pedraza S (2009) Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput Med Imaging Graph 33:304–311.  https://doi.org/10.1016/j.compmedimag.2009.02.001 CrossRefGoogle Scholar
  9. 9.
    Liao CC, Xiao F, Wong JM, Chiang IJ (2010) Computer-aided diagnosis of intracranial hematoma with brain deformation on computed tomography. Comput Med Imaging Graph 34:563–571.  https://doi.org/10.1016/j.compmedimag.2010.03.003 CrossRefGoogle Scholar
  10. 10.
    Li Y-H, Zhang L, Hu Q-M, Li H, Jia FC, Wu J (2012) Automatic subarachnoid space segmentation and hemorrhage detection in clinical head CT scans. Int J Comput Assist Radiol Surg 7:507–516CrossRefGoogle Scholar
  11. 11.
    Prakash KNB, Zhou S, Morgan TC, Daniel FH, Wieslaw LN (2012) Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique. Int J Comput Assist Radiol Surg 7:785–798CrossRefGoogle Scholar
  12. 12.
    Bhadauria HS, Singh A, Dewal ML (2013) An integrated method for hemorrhage segmentation from brain CT Imaging. Comput Electr Eng 39:1527–1536.  https://doi.org/10.1016/j.compeleceng.2013.04.010 CrossRefGoogle Scholar
  13. 13.
    Gillebert CR, Humphreys GW, Mantini D (2014) Automated delineation of stroke lesions using brain CT images. NeuroImage Clin 4:540–548.  https://doi.org/10.1016/j.nicl.2014.03.009 CrossRefGoogle Scholar
  14. 14.
    Shahangian B, Pourghassem H (2016) Automatic brain hemorrhage segmentation and classification algorithm based on weighted grayscale histogram feature in a hierarchical classification structure. Biocybern Biomed Eng 36:217–232.  https://doi.org/10.1016/j.bbe.2015.12.001 CrossRefGoogle Scholar
  15. 15.
    De Haan B, Clas P, Juenger H, Wilke M, Karnath HO (2015) Fast semi-automated lesion demarcation in stroke. NeuroImage Clin 9:69–74.  https://doi.org/10.1016/j.nicl.2015.06.013 CrossRefGoogle Scholar
  16. 16.
    Ray S, Kumar V, Ahuja C, Khandelwal N (2017) Intensity population based unsupervised hemorrhage segmentation from brain CT images. Expert Syst Appl 97:325–335CrossRefGoogle Scholar
  17. 17.
    Jnawali K, Arbabshirani MR, Rao N, Patel AA (2018) Deep 3D convolution neural network for CT brain hemorrhage classification. In: Medical imaging 2018: computer-aided diagnosis. International Society for Optics and Photonics, p 105751CGoogle Scholar
  18. 18.
    Gao X, Qian Y (2018) Segmentation of brain lesions from CT images based on deep learning techniques. In: Medical imaging 2018: biomedical applications in molecular, structural, and functional imaging. international society for optics and photonics, p 105782LGoogle Scholar
  19. 19.
    Gautam A, Raman B, Raghuvanshi S (2018) A hybrid approach for the delineation of brain lesion from CT images. Biocybern Biomed Eng 38:504–518CrossRefGoogle Scholar
  20. 20.
    Grewal M, Srivastava MM, Kumar P, Varadarajan S (2018) RADnet: radiologist level accuracy using deep learning for hemorrhage detection in CT scans. In: 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018). IEEE, pp 281–284Google Scholar
  21. 21.
    Chan TF, Vese LA (2001) Active contours without edges. IEEE Trans Image Process 10:266–277.  https://doi.org/10.1109/83.902291 CrossRefGoogle Scholar
  22. 22.
    Jenkinson M, Beckmann CF, Behrens TEJ, Woolrich MW, Smith SM (2012) Fsl Neuroimage 62:782–790CrossRefGoogle Scholar
  23. 23.
    Pal SK, King RA (1980) Image enhancement using fuzzy set. Electron Lett 16:376–378CrossRefGoogle Scholar
  24. 24.
    Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12:629–639CrossRefGoogle Scholar
  25. 25.
    Mumford D, Shah J (1989) Optimal approximations by piecewise smooth functions and associated variational problems. Commun Pure Appl Math 42:577–685CrossRefGoogle Scholar
  26. 26.
    Warfield SK, Zou KH, Wells WM (2004) Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23:903–921CrossRefGoogle Scholar
  27. 27.
    Kothari RU, Brott T, Broderick JP, Barsan WG, Sauerbeck LR, Zuccarello M, Khoury J (1996) The ABCs of measuring intracerebral hemorrhage volumes. Stroke 27(8):1304–1305CrossRefGoogle Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  • Manas Kumar Nag
    • 1
  • Saunak Chatterjee
    • 1
  • Anup Kumar Sadhu
    • 2
  • Jyotirmoy Chatterjee
    • 1
  • Nirmalya Ghosh
    • 3
    Email author
  1. 1.School of Medical Science and TechnologyIndian Institute of TechnologyKharagpurIndia
  2. 2.EKO DiagnosticsMedical College and Hospitals CampusKolkataIndia
  3. 3.Electrical EngineeringIndian Institute of TechnologyKharagpurIndia

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