Skip to main content

Advertisement

SpringerLink
Log in

Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans

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

Abstract

Purpose

Toward an efficient clinical management of hepatocellular carcinoma (HCC), we propose a classification framework dedicated to tumor necrosis rate estimation from dynamic contrast-enhanced CT scans. Based on machine learning, it requires weak interaction efforts to segment healthy, active and necrotic liver tissues.

Methods

Our contributions are two-fold. First, we apply random forest (RF) on supervoxels using multi-phase supervoxel-based features that discriminate tissues based on their dynamic in response to contrast agent injection. Second, we extend this technique in a hierarchical multi-scale fashion to deal with multiple spatial extents and appearance heterogeneity. It translates in an adaptive data sampling scheme combining RF and hierarchical multi-scale tree resulting from recursive supervoxel decomposition. By concatenating multi-phase features across the hierarchical multi-scale tree to describe leaf supervoxels, we enable RF to automatically infer the most informative scales without defining any explicit rules on how to combine them.

Results

Assessment on clinical data confirms the benefits of multi-phase information embedded in a multi-scale supervoxel representation for HCC tumor segmentation.

Conclusion

Dedicated but not limited only to HCC management, both contributions reach further steps toward more accurate multi-label tissue classification.

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

Access this article

Log in via an institution

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
Fig. 6

Similar content being viewed by others

Notes

  1. scikit-image implementation, http://scikit-image.org.

  2. scikit-learn implementation, http://scikit-learn.org/.

  3. http://maskslic.birving.com.

References

  1. Achanta R, Shaji A, Smith K, Lucchi A, Fua P, Susstrunk S (2012) SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Mach Intell 34:2274–2282

    Article  PubMed  Google Scholar 

  2. Akselrod-Ballin A, Galun M, Gomori JM, Filippi M, Valsasina P, Basri R, Brandt A (2009) Automatic segmentation and classification of multiple sclerosis in multichannel mri. IEEE Trans Biomed Eng 56(10):2461–2469

    Article  PubMed  Google Scholar 

  3. Avants BB, Epstein CL, Grossman M, Gee JC (2008) Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med Image Anal 12(1):26–41

    Article  CAS  PubMed  Google Scholar 

  4. Bauer S, Nolte LP, Reyes M (2011) Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization. In: Medical image computing and computer-assisted intervention, pp 354–361

  5. Beg MF, Miller MI, Trouvé A, Younes L (2005) Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int J Comput Vis 61:139–157

    Article  Google Scholar 

  6. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    Article  Google Scholar 

  7. Cantu M, Piardi T, Sommacale D, Ellero B, Woehl-Jaegle ML, Audet M, Ntourakis D, Wolf P, Pessaux P (2013) Pathologic response to non-surgical locoregional therapies as potential selection criteria for liver transplantation for hepatocellular carcinoma. Med Sci Monit Basic Res 18:273–284

    Google Scholar 

  8. Conze PH, Rousseau F, Noblet V, Heitz F, Memeo R, Pessaux P (2015) Semi-automatic liver tumor segmentation in dynamic contrast-enhanced CT scans using random forests and supervoxels. Mach Learn Med Imaging 9352:212–219

    Article  Google Scholar 

  9. Conze PH, Noblet V, Rousseau F, Heitz F, Memeo R, Pessaux P (2016) Random forests on hierarchical multi-scale supervoxels for liver tumor segmentation in dynamic contrast-enhanced CT scans. In: IEEE international symposium on biomedical imaging, pp 416–419

  10. Criminisi A, Shotton J, Konukoglu E (2012) Decision forests: a unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Found Trends Comput Graph Vis 7(2–3):81–227

    Google Scholar 

  11. Fang R, Zabih R, Raj A, Chen T (2012) Segmentation of liver tumor using efficient global optimal tree metrics graph cuts. In: Abdominal imaging. Computational and clinical applications, pp 51–59

  12. Forner A, Llovet JM, Bruix J (2012) Hepatocellular carcinoma. Lancet 379:1245–1255

    Article  PubMed  Google Scholar 

  13. Geremia E, Clatz O, Menze BH, Konukoglu E, Criminisi A, Ayache N (2011) Spatial decision forests for MS lesion segmentation in multi-channel magnetic resonance images. NeuroImage 57(2):378–390

    Article  PubMed  Google Scholar 

  14. Geremia E, Menze BH, Ayache N (2013) Spatially adaptive random forests. In: IEEE international symposium on biomedical imaging, pp 1344–1347

  15. Ho MH, Yu CY, Chung KP, Chen TW, Chu HC, Lin CK, Hsieh CB (2011) Locoregional therapy-induced tumor necrosis as a predictor of recurrence after liver transplant in patients with HCC. Ann Surg Oncol 18(13):3632–3639

    Article  PubMed  Google Scholar 

  16. Irving B, Cifor A, Papie BW, Franklin J, Anderson EM, Brady M, Schnabel JA (2014) Automated colorectal tumour segmentation in DCE-MRI using supervoxel neighbourhood contrast characteristics. Med Image Comput Comput Assist Interv 8673:609–616

    Google Scholar 

  17. Irving B, Franklin JM, Papie BW, Anderson EM, Sharma RA, Gleeson FV, Brady M, Schnabel JA (2016) Pieces-of-parts for supervoxel segmentation with global context: application to DCE-MRI tumour delineation. Med Image Anal 32:69–83

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lee J, Cai W, Singh A, Yoshida H (2010) Estimation of necrosis volumes in focal liver lesions based on multi-phase hepatic CT images. In: Virtual colonoscopy & abdominal imaging. Computational challenges & clinical opportunities, pp 60–67

  19. Machairas V, Baldeweck T, Walter T, Decencière E (2016) New general features based on superpixels for image segmentation learning. In: IEEE international symposium on biomedical imaging, pp 1409–1413

  20. Memeo R, de Blasi V, Cherkaoui Z, Dehlawi A, de Angelis N, Piardi T, Sommacale D, Marescaux J, Mutter D, Pessaux P (2016) New approaches in locoregional therapies for hepatocellular carcinoma. J Gastrointest Cancer 47:239–246

    Article  CAS  PubMed  Google Scholar 

  21. Montillo A, Shotton J, Winn J, Iglesias JE, Metaxas D, Criminisi A (2011) Entangled decision forests and their application for semantic segmentation of CT images. In: Information processing in medical imaging, pp 184–196

  22. Peter L, Pauly O, Chatelain P, Mateus D, Navab N (2015) Scale-adaptive forest training via an efficient feature sampling scheme. In: Medical image computing and computer-assisted intervention, pp 637–644

  23. Popovic A, de la Fuente M, Engelhardt M, Radermacher K (2007) Statistical validation metric for accuracy assessment in medical image segmentation. Int J Comput Assist Radiol Surg 2(3–4):169–181

    Article  Google Scholar 

  24. Raj A, Juluru K (2009) Visualization and segmentation of liver tumors using dynamic contrast MRI. In: IEEE conference of engineering in medicine and biology, pp 6985–6989

  25. Ronot M, Vilgrain V (2014) Hepatocellular carcinoma: diagnostic criteria by imaging techniques. Best Pract Res Clin Gastro-enterol 28(5):795–812

    Article  Google Scholar 

  26. Ronot M, Bouattour M, Wassermann J, Bruno O, Dreyer C, Larroque B, Castera L, Vilgrain V, Belghiti J, Raymond E, Faivre S (2014) Alternative response criteria (Choi, EASL and mRECIST) versus RECIST1.1 in patients with advanced hepatocellular carcinoma treated with Sorafenib. Oncologist 19:394–402

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shim JH, Kim KM, Lee YJ, Ko GY, Yoon HK, Sung KB, Park KM, Lee SG, Lim YS, Lee HC, Chung YH, Lee YS, Suh DJ (2010) Complete necrosis after transarterial chemoembolization could predict prolonged survival in patients with recurrent intrahepatic HCC after curative resection. Ann Surg Oncol 17(3):869–877

    Article  PubMed  Google Scholar 

  28. Shimizu A, Narihira T, Furukawa D, Kobatake H, Nawano S, Shinozaki K (2008) Ensemble segmentation using Adaboost with application to liver lesion extraction from a CT volume. In: Workshop on 3D segmentation in the clinic

  29. Tu Z, Bai X (2010) Auto-context and its application to high-level vision tasks and 3D brain image segmentation. IEEE Trans Pattern Anal Mach Intell 32(10):1744–1757

    Article  PubMed  Google Scholar 

  30. 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(7):903–921

  31. Wu W, Chen AY, Zhao L, Corso JJ (2014) Brain tumor detection and segmentation in a CRF framework with pixel-pairwise affinity and superpixel-level features. Int J Comput Assist Radiol Surg 9(2):241–253

  32. Yi Z, Criminisi A, Shotton J, Blake A (2009) Discriminative, semantic segmentation of brain tissue in MR images. In: Medical image computing and computer-assisted intervention, pp 558–565

  33. Zikic D, Glocker B, Konukoglu E, Criminisi A, Demiralp C, Shotton J, Thomas O, Das T, Jena R, Price S (2012) Decision forests for tissue-specific segmentation of high-grade gliomas in multi-channel MR. In: Medical image computing and computer-assisted intervention, pp 369–376

Download references

Acknowledgments

This work received the financial support from Fondation Arc, http://www.fondation-arc.org. Liver segmentation masks are provided by Visible Patient, www.visiblepatient.com.

Author information

Authors and Affiliations

  1. ICube UMR 7357, University of Strasbourg, CNRS, FMTS, 300 bd Sébastien Brant, 67412, Illkirch, France

    Pierre-Henri Conze, Vincent Noblet & Fabrice Heitz

  2. Institut Mines-Télécom, Télécom Bretagne, INSERM, LATIM UMR 1101, Technopôle Brest-Iroise, 29238, Brest, France

    François Rousseau

  3. Department of Hepato-Biliary and Pancreatic Surgery, Nouvel Hôpital Civil, Institut Hospitalo-Universitaire de Strasbourg, 1 place de l’Hôpital, 67000, Strasbourg, France

    Vito de Blasi, Riccardo Memeo & Patrick Pessaux

Authors
  1. Pierre-Henri Conze
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vincent Noblet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. François Rousseau
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabrice Heitz
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Vito de Blasi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Riccardo Memeo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Patrick Pessaux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pierre-Henri Conze.

Ethics declarations

Conflict of interest

Patrick Pessaux is speaker honorarium from Integra. Pierre-Henri Conze, Vincent Noblet, François Rousseau, Fabrice Heitz, Vito de Blasi and Riccardo Memeo declare that they have no conflict of interest.

Ethical standard

For this type of study, formal consent is not required.

Informed consent

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Conze, PH., Noblet, V., Rousseau, F. et al. Scale-adaptive supervoxel-based random forests for liver tumor segmentation in dynamic contrast-enhanced CT scans. Int J CARS 12, 223–233 (2017). https://doi.org/10.1007/s11548-016-1493-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-016-1493-1

Keywords

Search

Navigation