Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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Development of Bone Metastasis Detection Algorithm on Abdominal Computed Tomography Image using Pixel Wise Fully Convolutional Network

픽셀 단위 컨볼루션 네트워크를 이용한 복부 컴퓨터 단층촬영 영상 기반 골전이암 병변 검출 알고리즘 개발

  • Kim, Jooyoung (Department of Biomedical Engineering, Hanyang University) ;
  • Lee, Siyoung (Department of Medical Device Management and Research, Sungkyunkwan University) ;
  • Kim, Kyuri (Division of Biomedical Engineering, Konkuk University) ;
  • Cho, Kyeongwon (Department of Biomedical Engineering, Hanyang University) ;
  • You, Sungmin (Department of Biomedical Engineering, Hanyang University) ;
  • So, Soonwon (Department of Biomedical Engineering, Hanyang University) ;
  • Park, Eunkyoung (Smart Healthcare & Device Research Center, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • Cho, Baek Hwan (Department of Medical Device Management and Research, Sungkyunkwan University) ;
  • Choi, Dongil (Department of Medical Device Management and Research, Sungkyunkwan University) ;
  • Park, Hoon Ki (Department of Family Medicine, Hanyang University Medical Center) ;
  • Kim, In Young (Department of Biomedical Engineering, Hanyang University)
  • 김주영 (한양대학교 의생명공학전문대학원 생체의공학과) ;
  • 이시영 (성균관대학교 삼성융합의과학원 의료기기산업학과) ;
  • 김규리 (건국대학교 의료생명대학 의학공학부) ;
  • 조경원 (한양대학교 의생명공학전문대학원 생체의공학과) ;
  • 유승민 (한양대학교 의생명공학전문대학원 생체의공학과) ;
  • 소순원 (한양대학교 일반대학원 생체공학과) ;
  • 박은경 (성균관대학교 의과대학 삼성서울병원 스마트헬스케어의료기기융합연구센터) ;
  • 조백환 (성균관대학교 삼성융합의과학원 의료기기산업학과) ;
  • 최동일 (성균관대학교 삼성융합의과학원 의료기기산업학과) ;
  • 박훈기 (한양대학교병원 가정의학과) ;
  • 김인영 (한양대학교 의생명공학전문대학원 생체의공학과)
  • Received : 2017.11.21
  • Accepted : 2017.12.19
  • Published : 2017.12.31
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Abstract

This paper presents a bone metastasis Detection algorithm on abdominal computed tomography images for early detection using fully convolutional neural networks. The images were taken from patients with various cancers (such as lung cancer, breast cancer, colorectal cancer, etc), and thus the locations of those lesions were varied. To overcome the lack of data, we augmented the data by adjusting the brightness of the images or flipping the images. Before the augmentation, when 70% of the whole data were used in the pre-test, we could obtain the pixel-wise sensitivity of 18.75%, the specificity of 99.97% on the average of test dataset. With the augmentation, we could obtain the sensitivity of 30.65%, the specificity of 99.96%. The increase in sensitivity shows that the augmentation was effective. In the result obtained by using the whole data, the sensitivity of 38.62%, the specificity of 99.94% and the accuracy of 99.81% in the pixel-wise. lesion-wise sensitivity is 88.89% while the false alarm per case is 0.5. The results of this study did not reach the level that could substitute for the clinician. However, it may be helpful for radiologists when it can be used as a screening tool.

Keywords

References

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