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Interactive segmentation based on the live wire for 3D CT chest image analysis

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Abstract

Object

The definition of regions of interest (ROIs) such as suspect cancer nodules or lymph nodes in 3D MDCT chest images is often difficult because of the complexity of the phenomena that give rise to them. Manual slice tracing has been used commonly for such problems, but it is extremely time consuming, subject to operator biases, and does not enable reproducible results. Proposed automated 3D image-segmentation methods are generally application dependent, and even the most robust methods have difficulty in defining complex ROIs.

Materials and methods

The semi-automatic interactive paradigm known as live wire has been proposed by researchers, whereby the human operator interactively defines an ROI’s boundary, guided by an active automated method. We propose 2D and 3D live-wire methods that improve upon previously proposed techniques. The 2D method gives improved robustness and incorporates a search region to improve computational efficiency. The 3D method requires the operator to only consider a few 2D slices, with an automated procedure performing the bulk of the analysis.

Results

For tests run with five human operators on both 2D and 3D ROIs in 3D MDCT chest images, the reproducibility was  >97% and the ground-truth correspondence was at least 97%. The 2D live-wire approach was  ≥14 times faster than manual slice tracing, while the 3D method was  ≥28 times faster than manual slice tracing. Finally, we describe a computer-based tool and its application to 3D MDCT-based planning and follow-on live guidance of bronchoscopy.

Conclusion

The live-wire methods are efficient, reliable, easy to use, and applicable to a wide range of circumstances.

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References

  1. Kazerooni EA (2001). High resolution CT of the lungs. Am J Roentgenol 177(3): 501–519

    CAS  Google Scholar 

  2. Sihoe AD and Yim AP (2004). Lung cancer staging. J Surg Res 117(1): 92–106

    Article  PubMed  Google Scholar 

  3. Dalrymple NC, Prasad SR, Freckleton MW and Chintapalli KN (2005). Introduction to the language of three-dimensional imaging with multidetector CT. Radiographics 25(5): 1409–1428

    Article  PubMed  Google Scholar 

  4. Kiraly AP, Hoffman EA, McLennan G, Higgins WE and Reinhardt JM (2002). 3D human airway segmentation methods for clinical virtual bronchoscopy. Acad Radiol 9(10): 1153–1168

    Article  PubMed  Google Scholar 

  5. Fetita CI, Prêteux F, Beigelman-Aubry C and Grenier P (2004). Pulmonary airways: 3-D reconstruction from multislice CT and clinical investigation. IEEE Trans Med Imaging 23(11): 1353–1364

    Article  PubMed  Google Scholar 

  6. Brown M, McNitt-Gray M, Goldin J, Suh R, Sayre J and Aberle D (2001). Patient-specific models for lung nodule detection and surveillance in CT images. IEEE Trans Med Imaging 20(12): 1242–1250

    Article  PubMed  CAS  Google Scholar 

  7. McAdams HP, Goodman PC and Kussin P (1998). Virtual bronchoscopy for directing transbronchial needle aspiration of hilar and mediastinal lymph nodes: a pilot study. Am J Roentgenol 170: 1361–1364

    CAS  Google Scholar 

  8. Kiraly AP, Helferty JP, Hoffman EA, McLennan G and Higgins WE (2004). 3D path planning for virtual bronchoscopy. IEEE Trans Med Imaging 23(9): 1365–1379

    Article  PubMed  CAS  Google Scholar 

  9. Higgins WE, Chung N and Ritman EL (1992). LV-chamber extraction from 3-D CT images: accuracy and precision. Comput Med Imaging Graph 16(1): 17–26

    Article  PubMed  CAS  Google Scholar 

  10. Kass M, Witkin A and Terzopoulos D (1988). Snakes: active contour models. Int J Comput Vis 1(4): 321–331

    Article  Google Scholar 

  11. Cohen LD and Kimmel R (1997). Global minimum for active contour models: a minimal path approach. Int J Comput Vis 24(1): 57–78

    Article  Google Scholar 

  12. Kunert T, Heimann T, Schröter A, Schöbinger M, Böttger T, Thorn M, Wolf I, Engelmann U, Meinzer HP (2004) An interactive system for volume segmentation in computer-assisted surgery. In: Galloway RL (ed) SPIE medical imaging 2004: visualization, image-guided procedures, and display, vol 5367, pp 799–809

  13. McInerney T, Akhavan-Sharif MR (2006) Sketch initialized snakes for rapid, accurate and repeatable interactive medical image segmentation. In: 3rd IEEE international symposium on biomedical imaging 2006: Macro to Nano, pp 398–401

  14. Mortensen EN, Morse BS, Barrett WA and Udupa JK (1992). Adaptive boundary detection using “live-wire” two-dimensional dynamic programming. IEEE Proc Comput Cardiol 11(14): 635–638

    Article  Google Scholar 

  15. Udupa JK, Samarasekera S, Barrett WA (1992) Boundary detection via dynamic programming. In: Robb RA (ed) SPIE visualization in biomedical computing, vol 1808, pp 33–39

  16. Mortensen EN, Barrett WA (1995) Intelligent scissors for image composition. In: Proceedings of ACM SIGGRAPH95: 22nd international conference of computer graphics and interactive techniques. pp 191–198

  17. Falcão AX, Udupa JK, Samarasekera S, Hirsch BE (1996) User-steered image boundary segmentation. In: Loew MH, Hanson KM (eds) SPIE medical imaging 1996: image processing, vol 2710, pp 278–288

  18. Falcão AX, Udupa JK (1997) Segmentation of 3D objects using live-wire. In: Hanson KM (ed) SPIE medical imaging 1997: image processing, vol 3034, pp 228–239

  19. Falcão AX, Udupa JK, Samarasekera S and Sharma S (1998). User-steered image segmentation paradigms: Live wire and live lane. Graphical Models Image Process 60(4): 233–260

    Article  Google Scholar 

  20. Mortensen EN and Barrett WA (1998). Interactive segmentation with intelligent scissors. Graphical Models Image Process 60(5): 349–384

    Article  Google Scholar 

  21. Falcão AX and Udupa JK (2000). A 3D generalization of user-steered live-wire segmentation. Med Image Anal 4(4): 389–402

    Article  PubMed  Google Scholar 

  22. Falcão AX, Udupa JK and Miyazawa FK (2000). An ultra-fast user-steered live-wire segmentation paradigm: Live wire on the fly. IEEE Trans Med Imaging 19(1): 55–62

    Article  PubMed  Google Scholar 

  23. Mortensen EN, Reese LJ, Barrett WA (2000) Intelligent selection tools. In: IEEE Proc Comput Soc Conf on Comput Vis Pattern Recogn, pp 776–777

  24. Barrett WA, Reese LJ, Mortensen EN (2002) Intelligent segmentation tools. In: IEEE Proc 2002 international symposium on biomedical Imaging, pp 217–220

  25. Haenselmann T, Effelsberg W (2003) Wavelet-based semi-automatic live-wire segmentation. In: Rogowitz BE, Pappas TN (eds) SPIE electronic imaging 2003: human vision and electronic imaging VIII, vol 5007, pp 260–269

  26. Kang H-W and Shin S-Y (2003). Enhanced lane: Interactive image segmentation by incremental path map construction. Graphical Models 64(5): 282–303

    Article  Google Scholar 

  27. Chodorowski A, Mattsson U, Langille M, Hamarneh G (2005) Color lesion boundary detection using live wire. In: Fitzpatrick JM, Reinhardt JM (eds) SPIE medical imaging 2005: image processing, vol 5747, pp 1589–1596

  28. Wieclawek W (2005) Live-wire method with FCM classification. In: IEEE Proceedings of international conference on mixed design of integrated circuits and system, pp 756–776

  29. Wieclawek W (2006) Live-wire method with wavelet cost map definition for MRI images, 2006. In: IFAC Workshop on Programmable Devices and Embedded Systems, PDeS 2006, Brno

  30. Rose C and Binder K (2005). SAMS Teach Yourself Adobe Photoshop CS2 in 24 Hours. Sams Publishing, Indianapolis

    Google Scholar 

  31. Schenk A, Prause G, Peitgen H-O (2000) Efficient semiautomatic segmentation of 3D objects in medical images. In: MICCAI’00: Proceeding of the third international conference on medical image computing and computer-assisted intervention. Springer, Heidelberg, London, pp 186–195

  32. König S, Hesser J (2005) 3D live-wires on pre-segmented volume data. In: Fitzpatrick JM, Reinhardt JM (eds) SPIE medical imaging 2005: image processing, vol 5747, pp 1674–1681

  33. König S and Hesser J (2006). 3D live-wires on mosaic volumes. Stud Health Technol Inform 119: 264–266

    PubMed  Google Scholar 

  34. Salah Z, Orman J, Bartz D (2005) Live-wire revisited. In: Workshop Bildverarbeitung für die Medizin, Berlin

  35. Hamarneh G, Yang J, McIntosh C, Langille M (2005) 3D live-wire-based semi-automatic segmentation of medical images. In: SPIE medical imaging 2005: image processing, vol 5747, pp 1597–1603

  36. Souza A, Udupa JK, Grevera G, Sun Y, Odhner D, Suri N, Schnall MD (2006) Iterative live wire and live snake: new user-steered 3D image segmentation paradigms. In: Reinhardt JM, Pluim PW (eds) SPIE medical imaging 2006: image processing, vol 6144, pp 61443N1–61443N7

  37. Helferty JP, Sherbondy AJ, Kiraly AP, Higgins WE (2007) Computer-based system for the virtual-bronchoscopic guidance of bronchoscopy. Computer Vision and Image Understanding (in press)

  38. Merritt SA, Rai L, Gibbs JD, Yu K-C, Higgins WE (2007) Method for continuous guidance of endoscopy. In: Manduca A, Hu XP (eds) SPIE medical imaging 2007: physiology, function, and structure from medical images, vol 6511

  39. Cormen TH (2001). Introduction to algorithms. MIT, Cambridge

    Google Scholar 

  40. Gonzalez RC and Woods RE (2002). Digital image processing, 2nd edn. Addison Wesley, Reading

    Google Scholar 

  41. Lu K, Higgins WE (2006) Improved 3D live-wire method with application to 3D CT chest images. In: Reinhardt JM, Pluim JP (eds) SPIE medical imaging 2006: image processing, vol 6144, pp 189–203

  42. Yu KC, Ritman EL, Higgins WE (2004) 3D model-based vasculature analysis using differential geometry. In: IEEE International symposium on biomedical imaging, vol 1, pp 177–180

  43. Yu KC, Ritman EL, Higgins WE (2005) System for 3D visualization and data mining of large vascular trees. In: Javadi B, Okano F, Son J (eds) SPIE optics east 2005: three-dimensional TV, video, and display IV, vol 6016, pp 101–115

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Correspondence to William E. Higgins.

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Lu, K., Higgins, W.E. Interactive segmentation based on the live wire for 3D CT chest image analysis. Int J CARS 2, 151–167 (2007). https://doi.org/10.1007/s11548-007-0129-x

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