한국통신학회논문지 (The Journal of Korean Institute of Communications and Information Sciences)

한국통신학회 (The Korean Institute of Commucations and Information Sciences)
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3차원 흉부 CT에서 추체 골 전이 병변에 대한 반자동 검출 기법 및 분류 시스템 개발

Development of a Semi-Automated Detection Method and a Classification System for Bone Metastatic Lesions in Vertebral Body on 3D Chest CT

  • 김영재 (국립암센터 융합기술연구부 의공학연구과, 광운대학교 플라즈마 바이오 디스플레이학과) ;
  • 이승현 (광운대학교 플라즈마 바이오 디스플레이학과) ;
  • 최자영 (서울대학교병원 영상의학과) ;
  • 선혜영 (서울대학교병원 영상의학과) ;
  • 김광기 (국립암센터 융합기술연구부 의공학연구과)
  • 투고 : 2013.07.31
  • 심사 : 2013.10.10
  • 발행 : 2013.10.31
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초록

골 전이 암은 여러 장기에 생긴 암이 질병이 경과함에 따라 뼈로 옮아가는 것으로서, 암 환자에게서 주로 발생하는 합병증 중 하나이다. 골 전이는 골 용해성 전이와 골 형성성 전이로 구분되며, CT에서 골 전이의 진단은 임상적으로 매우 유용할 수 있으나, 많은 판독건수로 인하여 중요한 병변이 간과되는 경우가 많고, 이를 통해 골 전이 암을 조기에 진단하지 못하는 경우가 발생할 수 있다. 이에 본 논문에서는 흉부 CT의 단층 영상들을 3차원 볼륨 데이터로 구성하여 3차원 영상처리 알고리즘을 적용하여 골 전이 병변을 검출하고 3차원 가시화를 수행하였으며, 총 10개 데이터에 대해 민감도를 측정한 결과, 골 형성성 병변이 평균 94.1%, 골 용해성 병변이 평균 90.0%의 값을 나타내어 골 전이 진단에서의 활용에 대한 높은 가능성과 잠재적인 유용성을 확인할 수 있었다.

Metastatic bone cancer, the cancer which occurred in the various organs and progressively spread to bone, is one of the complications in cancer patients. This cancer is divided into the osteoblast and osteolytic metastasis. Although Computer Tomography(CT) could be an useful tool in diagnosis of bone metastasis, lesions are often missed by the visual inspection and it makes clinicians difficult to detect metastasis earlier. Therefore, in this study, we construct a three-dimensional(3D) volume rendering data from tomography images of the chest CT, and apply a 3D based image processing algorithm to them for detection bone metastasis lesions. Then we perform a three-dimensional visualization of the detected lesions.From our test using 10 clinical cases, we confirmed 94.1% of average sensitivity for osteoblast, and 90.0% of average sensitivity, respectively. Consequently, our findings showed a promising possibility and potential usefulness in diagnosis of metastastic bone cancer.

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참고문헌

  1. G. R. Mundy, "Metastasis: Metastasis to bone: causes, consequences and therapeutic opportunities," Nature Reviews Cancer, vol. 2, no. 8, pp. 584-593, Aug. 2002. https://doi.org/10.1038/nrc867
  2. K. J. Kim, H. S. Kang, Y. I. Kim, and B. J. Shin, "Clinical analysis of metastatic tumors of bone," J. The Korean Bone, Joint Tumor Soc., vol. 7, no. 4, pp. 133-138, Dec. 2001.
  3. S. H. Lee, H. S. Kim, J. K. Jung, H. G. Kim, J. S. Kim, and H. K. Lee, "Early diagnosis of metastatic bone tumor using bone marrow scan - the diagnostic utilities of bone marrow scan," J. Korean Bone, Joint Tumor Soc., vol. 3, no. 3, pp. 166-171, Sep. 1997.
  4. B. Haubold-Reuter, S. Duewell, B. Schilcher, B. Marincek, and G. Schulthess, "The value of bone scintigraphy, bone marrow scintigraphy and fast spin-echo magnetic resonance imaging in staging of patients with malignant solid tumours: a prospective study," European J. Nuclear Medicine, vol. 20, no. 11, pp. 1063-1069, Nov. 1993.
  5. K. Doi, "Current status and future potential of computer-aided diagnosis in medical imaging," British J. Radiology, vol. 78, no. 1, pp. s3-s19, Jan. 2005. https://doi.org/10.1259/bjr/82933343
  6. S. Y. Lee, "Computer-aided diagnosis technology," The World of Electricity, vol. 60, no. 7, pp. 59-64, July. 2011.
  7. S. D. O'Connor, J. Yao, and R. M. Summers, "Lytic metastases in thoracolumbar spine: computer-aided detection at CT-preliminary study1," Radiology, vol. 242, no. 3, pp. 811-816, Mar. 2007. https://doi.org/10.1148/radiol.2423060260
  8. S. F. Huang and K. H. Chiang, "Automatic detection of bone metastasis in vertebrae by using CT images," In Proceedings of the World Congress on Engineering, pp. 1166-1171, London, U.K., July. 2012.
  9. W. C. Scarfe, A. G. Farman, and P. Sukovic, "Clinical applications of cone-beam computed tomography in dental practice," J. Canadian Dental Assoc., vol. 72, no. 1, pp. 75-80, Feb. 2006.
  10. M. Quirynen, Y. Lamoral, C. Dekeyser, P. Peene, D. van Steenberghe, J. Bonte, and A. L. Baert, "CT scan standard reconstruction technique for reliable jaw bone volume determination," Int. J. Oral, Maxillofacial Implants, vol. 5, no. 4, pp. 384-389, Jan. 1990.
  11. R. Adams and L. Bischof, "Seeded region growing," IEEE Trans. Pattern Anal. Mach. Intell., vol. 16, no. 6, pp. 641-647, June 1994. https://doi.org/10.1109/34.295913
  12. J. Fan, G. Zeng, M. Body, and M. S. Hacid, "Seeded region growing: an extensive and comparative study," Pattern Recognition Lett., vol. 26, no. 8, pp. 1139-1156, June. 2005. https://doi.org/10.1016/j.patrec.2004.10.010
  13. M. Tuceryan and A. K. Jain, Handbook of Pattern Recognition and Computer Vision, pp. 235-276, 1993.
  14. M. Welling, Fisher linear discriminant analysis, [Online]. Available: http://www.ics.uci.edu/-welling/classnotes/pape rs_class/Fisher-LDA.pdf.
  15. N. Y. Choi, S. H. Lee, J. W. Kang, T. G. Oh, and S. H. Lee, "3D Vessel visualization based on its geometric characteristics in CTA image using direct volume rendering," in Proc. KICS Winter Conf., pp. 639-640, Yongpyung, Korea, Jan. 2013.
  16. S. H. Cha, "Comprehensive survey on distance/similarity measures between probability density functions," Int. J. Math. Models Methods Applied Sci., vol. 1, no. 4, pp. 300-307, Apr. 2007.