Skip to main content
Springer Nature Link
Log in

Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

High mortality rate due to liver cirrhosis has been reported over the globe in the previous years. Early detection of cirrhosis may help in controlling the disease progression toward hepatocellular carcinoma (HCC). The lack of trained CT radiologists and increased patient population delays the diagnosis and further management. This study proposes a computer-aided diagnosis system for detecting cirrhosis and HCC in a very efficient and less time-consuming approach.

Methods

Contrast-enhanced CT dataset of 40 patients (n = 40; M:F = 5:3; age = 25–55 years) with three groups of subjects: healthy (n = 14), cirrhosis (n = 12) and cirrhosis with HCC (n = 14), were retrospectively analyzed in this study. A novel method for the automatic 3D segmentation of liver using modified region-growing segmentation technique was developed and compared with the state-of-the-art deep learning-based technique. Further, histogram parameters were calculated from segmented CT liver volume for classification between healthy and diseased (cirrhosis and HCC) liver using logistic regression. Multi-phase analysis of CT images was performed to extract 24 temporal features for detecting cirrhosis and HCC liver using support vector machine (SVM).

Results

The proposed method produced improved 3D segmentation with Dice coefficient 90% for healthy liver, 86% for cirrhosis and 81% for HCC subjects compared to the deep learning algorithm (healthy: 82%; cirrhosis: 78%; HCC: 70%). Standard deviation and kurtosis were found to be statistically different (p < 0.05) among healthy and diseased liver, and using logistic regression, classification accuracy obtained was 92.5%. For detecting cirrhosis and HCC liver, SVM with RBF kernel obtained highest slice-wise and patient-wise prediction accuracy of 86.9% (precision = 0.93, recall = 0.7) and 80% (precision = 0.86, recall = 0.75), respectively, than that of linear kernel (slice-wise: accuracy = 85.4%, precision = 0.92, recall = 0.67; patient-wise: accuracy = 73.33%, precision = 0.75, recall = 0.75).

Conclusions

The proposed computer-aided diagnosis system for detecting cirrhosis and hepatocellular carcinoma (HCC) showed promising results and can be used as effective screening tool in medical image analysis.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Jemal A, Bray F, Center MM et al (2011) Global cancer statistics. CA Cancer J Clin 61:69–90

    Article  PubMed  Google Scholar 

  2. Mokdad AA, Lopez AD, Shahraz S et al (2014) A systematic analysis. BMC Med 12:1–24. https://doi.org/10.1186/s12916-014-0145-y

    Article  Google Scholar 

  3. Zhang XF, Meng B, Qi X et al (2009) Prognostic factors after liver resection for hepatocellular carcinoma with hepatitis B virus-related cirrhosis: the surgeon’s role in survival. Eur J Surg Oncol 35:622–628. https://doi.org/10.1016/j.ejso.2008.08.003

    Article  PubMed  Google Scholar 

  4. Egger J (2013) Manual refinement system for graph-based segmentation results in the medical domain. J Med Syst 36:2829–2839. https://doi.org/10.1007/s10916-011-9761-7.Manual

    Article  Google Scholar 

  5. Campadelli P, Casiraghi E, Esposito A (2009) Liver segmentation from computed tomography scans: a survey and a new algorithm. Artif Intell Med 45:185–196. https://doi.org/10.1016/j.artmed.2008.07.020

    Article  PubMed  Google Scholar 

  6. Heimann T, Van Ginneken B, Styner MA et al (2009) Comparison and evaluation of methods for liver segmentation from CT datasets. IEEE Trans Med Imaging 28:1251–1265. https://doi.org/10.1109/TMI.2009.2013851

    Article  PubMed  Google Scholar 

  7. Zhou JY, Wong DWK, Ding F et al (2010) Liver tumour segmentation using contrast-enhanced multi-detector CT data: performance benchmarking of three semiautomated methods. Eur Radiol 20:1738–1748. https://doi.org/10.1007/s00330-010-1712-z

    Article  PubMed  Google Scholar 

  8. Kumar SS, Moni RS, Rajeesh J (2013) Automatic liver and lesion segmentation: a primary step in diagnosis of liver diseases. Signal Image Video Process 7:163–172. https://doi.org/10.1007/s11760-011-0223-y

    Article  Google Scholar 

  9. Ruskó L, Bekes G, Németh G, Fidrich M (2007) Fully automatic liver segmentation for contrast-enhanced CT images MICCAI Wshp 3D segmentation. Clin A Gd Chall 2:143–150

    Google Scholar 

  10. Zhao Y, Zan Y, Wang X, Li G (2010) Fuzzy C-means clustering-based multilayer perceptron neural network for liver CT images automatic segmentation. Chin Control Decis Conf CCDC 2010:3423–3427. https://doi.org/10.1109/CCDC.2010.5498558

    Article  Google Scholar 

  11. Goryawala M, Gulec S, Bhatt R et al (2014) A low-interaction automatic 3D liver segmentation method using computed tomography for selective internal radiation therapy. Biomed Res Int. https://doi.org/10.1155/2014/198015

    Article  PubMed  PubMed Central  Google Scholar 

  12. Conze P, Noblet V, Rousseau F, et al (2016) Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans. In: 2016 IEEE 13th International symposium on biomedical imaging (ISBI), pp 416–419

  13. Moghbel M, Mashohor S, Mahmud R, Saripan BMI (2016) Automatic liver segmentation on computed tomography using random walkers for treatment planning. EXCLI J 15:500–517

    PubMed  PubMed Central  Google Scholar 

  14. Wu W, Zhou Z, Wu S, Zhang Y (2016) Automatic liver segmentation on volumetric CT images using supervoxel-based graph cuts. Comput Math Methods Med. https://doi.org/10.1155/2016/9093721

    Article  PubMed  PubMed Central  Google Scholar 

  15. Hoogi A, Subramaniam A, Veerapaneni R, Rubin DL (2017) Adaptive estimation of active contour parameters using convolutional neural networks and texture analysis. IEEE Trans Med Imaging 36:781–791. https://doi.org/10.1109/TMI.2016.2628084

    Article  PubMed  Google Scholar 

  16. Chan TF, Vese LA (1977) Donald Middleton, LDS Br Dent. J 142:73. https://doi.org/10.1109/83.902291

  17. Mala K, Sadasivam V (2005) Automatic segmentation and classification of diffused liver diseases using wavelet based texture analysis and neural network. In: Proceedings INDICON 2005 An International Conference IEEE India Council, 2005, pp 216–219. https://doi.org/10.1109/INDCON.2005.1590158

  18. Luo S, Hu Q, He X et al (2009) Automatic liver parenchyma segmentation from abdominal CT images using support vector machines. ICME Int Conf Complex Med Eng C. https://doi.org/10.1109/ICCME.2009.4906625

    Article  Google Scholar 

  19. Christ PF, Elshaer MEA, Ettlinger F, et al (2016) Automatic liver and lesion segmentation in CT using cascaded fully convolutional neural networks and 3D conditional random fields. Lecture notes computer science (including subset lecture notes artificial intelligence lecture notes bioinformatics) 9901 LNCS:415–423. https://doi.org/10.1007/978-3-319-46723-8_48

  20. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention—MICCAI, pp 234–241

  21. Hameed RS, KS S (2012) Assessment of neural network based classifiers to diagnose focal liver lesions using CT images. Proc Eng 38:4048–4056. https://doi.org/10.1016/j.proeng.2012.06.464

    Article  Google Scholar 

  22. Stoitsis J, Valavanis I, Mougiakakou SG et al (2006) Computer aided diagnosis based on medical image processing and artificial intelligence methods. Nucl Instrum Methods Phys Res 569:591–595. https://doi.org/10.1016/j.nima.2006.08.134

    Article  CAS  Google Scholar 

  23. Mougiakakou SG, Valavanis IK, Nikita A, Nikita KS (2007) Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers. Artif Intell Med 41:25–37. https://doi.org/10.1016/j.artmed.2007.05.002

    Article  PubMed  Google Scholar 

  24. Chen Y, Luo J, Dong C et al (2013) Computer-aided diagnosis and quantification of cirrhotic livers based on morphological analysis and machine learning. Comput Math Methods Med. https://doi.org/10.1155/2013/264809

    Article  PubMed  PubMed Central  Google Scholar 

  25. Bilello M, Gokturk SB, Desser T et al (2004) Automatic detection and classification of hypodense hepatic lesions on contrast-enhanced venous-phase CT. Med Phys 31:2584–2593. https://doi.org/10.1118/1.1782674

    Article  PubMed  Google Scholar 

  26. Ruskó L, Perényi Á (2014) Automated liver lesion detection in CT images based on multi-level geometric features. Int J CARS 9:577–593. https://doi.org/10.1007/s11548-013-0949-9

    Article  Google Scholar 

  27. Quatrehomme A, Millet I, Hoa D, et al (2012) Assessing the classification of liver focal lesions by using multi-phase computer tomography scans. In: MICCAI international workshop on medical content-based retrieval for clinical decision support, pp 80–91

  28. Duda D, Krętowski M, Bezy-wendling J (2006) Texture characterization for hepatic tumor recognition in multiphase CT. Biocybern Biomed Eng 26:15–24

    Google Scholar 

  29. Ye J, Sun Y, Wang S, et al (2009) Multi-phase CT image based hepatic lesion diagnosis by SVM. In: 2nd IEEE international conference on biomedical engineering and informatics, pp 1–5

  30. Chi Y, Zhou J, Venkatesh KS et al (2013) Computer-aided focal liver lesion detection. Int J CARS 8:511–525. https://doi.org/10.1007/s11548-013-0832-8

    Article  Google Scholar 

  31. Soler L, Hostettler A, Agnus V, et al (2012) 3D Image reconstruction for comparison of algorithm database : a patient-specific anatomical and medical image database, p 67091

  32. Gonzalez and Woods Image Processing 2 edition.pdf

  33. Chen Y, Wang Z, Zhao W, Yang X (2009) Liver segmentation from CT images based on region growing method. In: 3rd International conference on bioinformatics and biomedical engineering, Beijing, pp 1–4. https://doi.org/10.1109/ICBBE.2009.5163018

  34. Tsiprin MG, Irgen LA, Kerch GM, Silis YY (1980) Apparatus for isometric heating and thermomechanical testing of films and fibers. Mech Compos Mater 15:649–651. https://doi.org/10.1007/BF00605598

    Article  Google Scholar 

  35. Lee JH, Lee JM, Kim SJ et al (2012) Enhancement patterns of hepatocellular carcinomas on multiphasic multidetector row CT: comparison with pathological differentiation. Br J Radiol 85:e573–e583. https://doi.org/10.1259/bjr/86767895

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Marrero JA, Ahn J, Reddy RK (2014) ACG clinical guideline: the diagnosis and management of focal liver lesions. Am J Gastroenterol 109:1–20. https://doi.org/10.1038/ajg.2014.213

    Article  Google Scholar 

  37. Achanta R, Shaji A, Smith K et al (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34:2274–2281. https://doi.org/10.1109/TPAMI.2012.120

    Article  PubMed  Google Scholar 

  38. Lloyd SP (1982) Least squares quantization in PCM. IEEE Trans Inf Theory 28:129–137. https://doi.org/10.1109/TIT.1982.1056489

    Article  Google Scholar 

  39. Cortes C, Vapnik V (1995) Supprot-vector networks. Mach Learn 297:273–297. https://doi.org/10.1111/j.1747-0285.2009.00840.x

    Article  CAS  Google Scholar 

  40. Pedregosa F, Weiss R, Brucher M (2011) Nscep 12:2825–2830. https://doi.org/10.1007/s13398-014-0173-7.2

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering, Indian Institute of Technology Delhi, New Delhi, India

    Akash Nayak, Jayanth Culli & Sumeet Agarwal

  2. IBM Research, Bangalore, India

    Akash Nayak

  3. Centre for Biomedical Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi, India

    Esha Baidya Kayal, Manish Arya & Amit Mehndiratta

  4. Department of Radiology, Medanta The Medicity, Gurgaon, India

    Sonal Krishan

  5. Department of Biomedical Engineering, All India Institute of Medical Sciences, New Delhi, India

    Amit Mehndiratta

Authors
  1. Akash Nayak
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Esha Baidya Kayal
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Manish Arya
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Jayanth Culli
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Sonal Krishan
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Sumeet Agarwal
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Amit Mehndiratta
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Amit Mehndiratta.

Ethics declarations

Conflicts of interest

The authors have no relevant conflicts of interest to disclose regarding this study. AN performed this study during his stay at IIT Delhi. AN is currently employed with IBM Research, India.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nayak, A., Baidya Kayal, E., Arya, M. et al. Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT. Int J CARS 14, 1341–1352 (2019). https://doi.org/10.1007/s11548-019-01991-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-019-01991-5

Keywords