DOI QR코드

DOI QR Code

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

Residual Multi-Dilated Recurrent Convolutional U-Net을 이용한 전자동 심장 분할 모델 분석

  • 임상헌 (계명대학교 의용공학과) ;
  • 이명숙 (계명대학교 타불라라사칼리지(과학과기술))
  • Received : 2019.09.18
  • Accepted : 2019.11.09
  • Published : 2020.02.29

Abstract

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.

References

  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.
  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.
  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.
  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.
  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.