정보처리학회논문지:컴퓨터 및 통신 시스템 (KIPS Transactions on Computer and Communication Systems)

  • 제9권2호
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  • Pages.37-44
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  • 2020
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  • 2287-5891(pISSN)
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  • 2734-049X(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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Residual Multi-Dilated Recurrent Convolutional U-Net을 이용한 전자동 심장 분할 모델 분석

Fully Automatic Heart Segmentation Model Analysis Using Residual Multi-Dilated Recurrent Convolutional U-Net

  • 임상헌 (계명대학교 의용공학과) ;
  • 이명숙 (계명대학교 타불라라사칼리지(과학과기술))
  • 투고 : 2019.09.18
  • 심사 : 2019.11.09
  • 발행 : 2020.02.29
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 딥 러닝 기반의 전-자동 심장 분할 알고리즘을 제안한다. 본 논문에서 제안하는 딥 러닝 모델은 기존 U-Net에 residual recurrent convolutional block과 residual multi-dilated convolutional block을 삽입하여 성능을 개선한 모델이다. 모델의 성능은 테스트 데이터 세트를 전-자동 분할한 결과와 영상의학 전문가의 수동 분할 결과를 비교하여 분석하였다. CT 영상에서 평균 96.88%의 DSC, 95.60%의 precision과 97.00%의 recall 결과를 얻었다. 분할된 영상은 3차원 볼륨 렌더링 기법을 적용하여 시각화한 후 관찰하여 분석할 수 있었다. 실험 결과를 통해 제안된 알고리즘이 다양한 심장 하부 구조를 분할하기에 효과적인 것을 알 수 있었다. 본 논문에서 제안하는 알고리즘이 전문의 또는 방사선사의 임상적 보조역할을 수행할 수 있을 것으로 기대한다.

In this paper, we proposed that a fully automatic multi-class whole heart segmentation algorithm using deep learning. The proposed method is based on U-Net architecture which consist of recurrent convolutional block, residual multi-dilated convolutional block. The evaluation was accomplished by comparing automated analysis results of the test dataset to the manual assessment. We obtained the average DSC of 96.88%, precision of 95.60%, and recall of 97.00% with CT images. We were able to observe and analyze after visualizing segmented images using three-dimensional volume rendering method. Our experiment results show that proposed method effectively performed to segment in various heart structures. We expected that our method can help doctors and radiologist to make image reading and clinical decision.

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참고문헌

  1. 2017 Cause of Death Statistics, Seoul: National Statistical Office, pp. 10, 2018.
  2. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," Proceeding of The IEEE Conference on Computer Vision and Pattern Recognition, pp.580-587, 2014.
  3. H. W. Noh, S. H. Hong, and B. H. Han, "Learning Deconvolution Network for Segmentation," Proceeding of The IEEE International Conference on Computer Vision, pp. 1520-1528, 2015.
  4. D. Cireşan, U. Meier, and J. Schmidhuber, "Multi-column Deep Neural Networks for Image Classification," Proceeding of The IEEE Conference on Computer Vision a (32nd Pattern Recognition, pp.3642-3649, 2012.
  5. M. Havaei, A. Davy, D. W. Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P. M. Jodoin, and H. Larochelle, "Brain Tumor Segmentation with Deep Neural Networks," Medical Image Analysis, Vol. 35, pp. 18-31, 2017. https://doi.org/10.1016/j.media.2016.05.004
  6. A. F. Frangi, W. J. Niessen, and M. A. Viergever, "Three-Dimentional Modeling for Functional Analysis of Cardiac Images: A Review," IEEE Transactions on Medical Imaging, Vol.20, pp.2-5, 2001. https://doi.org/10.1109/42.906421
  7. S. Sivakumar and C. Chandrasekar, "Lung Nodule Detection using Fuzzy Clustering and Support Vector Machines," International Journal of Engineering and Technology, Vol. 5, No.1, pp.179-185, 2013.
  8. O. Ronneberger, P. Fischer, and T. Brox, "U-Net: Convolutional Networks for Biomedical Image Segmentation," Proceeding of International Conference on Medical Image Computer-Assisted Intervention, Vol.9351, pp.234-241, 2015.
  9. J. Long, E. Shelhamer, and T. Darrell, "Fully Convolutional Networks for Semantic Segmentation," Proceeding of The IEEE Conference on Computer Vision and Pattern Recognition, pp.3431-3440, 2015.
  10. Google Colaboratory, https://colab.research.google.com, (Accessed Aug, 6, 2019)
  11. M. Liang, and X. Hu, "Recurrent Convolutional Neural Network for Object Recognition," Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, pp.3367-3375, 2015.
  12. F. Yu, V. Koltun, and T. Funkhouser, "Dilated Residual Networks," Proceeding of The IEEE Conference on Computer Vision and Pattern Recognition, pp. 472-480, 2017.
  13. P. Mildenberger, M. Eichelberg, and E. Martin, "Introduction to the DICOM standard," European Radiology, Vol.12, pp. 920-927, 2002. https://doi.org/10.1007/s003300101100
  14. S. H. Lim, H. S. Choi, H. J. Bae, S. K. Jung, J. K, Jung, and M. S. Lee, "Multi-Class Whole Heart Segmentation using Residual Multi-dilated Convolution U-net," The KIPS Spring Conference 2019, Vol.26, No.1. pp.508-510, 2019.
  15. X. Zhuang and J. Shen, "Multi-scale patch and multimodality atlases for whole heart segmentation of MRI," Medical Image Analysis, Vol.31, pp.77-87, 2016. https://doi.org/10.1016/j.media.2016.02.006
  16. D. P. Kingma and J. Ba, "Adam: A Method for Stochastic Optimization," Proceeding of International Conference on Learning Representations, 2015.
  17. C. H. Sudre, W. Li, T. Vercauteren, S. Ourselin, and M. J. Cardoso, "Generalized Dice Overlap as a Deep Learning Loss Function for Highly Unbalanced Segmentations," Proceeding of Deep Learning in Medical Image Analysis and Multimodal Laerning for Clinical Decision Support, pp.240-248, 2017.