Dense Volume-to-Volume Vascular Boundary Detection

详细信息    查看全文

• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9902
• 期：1
• 页码：371-379
• 全文大小：1,790 KB
• 参考文献：1.Arbelaez, P., Maire, M., Fowlkes, C., Malik, J.: Contour detection and hierarchical image segmentation. PAMI 33(5), 898–916 (2011)CrossRef
  2.Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: BMVC (2014)
  3.Dollár, P., Zitnick, C.L.: Fast edge detection using structured forests. In: PAMI (2015)
  4.Hariharan, B., Arbeláez, P., Girshick, R., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: CVPR (2014)
  5.Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., Darrell, T.: Caffe: convolutional architecture for fast feature embedding. preprint arXiv:​1408.​5093 (2014)
  6.Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: AISTATS (2015)
  7.Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR (2014)
  8.Martin, D.R., Fowlkes, C.C., Malik, J.: Learning to detect natural image boundaries using local brightness, color, and texture cues. PAMI 26(5), 530–549 (2004)CrossRef
  9.Merkow, J., Tu, Z., Kriegman, D., Marsden, A.: Structural edge detection for cardiovascular modeling. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 735–742. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24574-4_​88 CrossRef
  10.Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24574-4_​28 CrossRef
  11.Roth, H.R., Lu, L., Farag, A., Shin, H.-C., Liu, J., Turkbey, E.B., Summers, R.M.: DeepOrgan: multi-level deep convolutional networks for automated pancreas segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 556–564. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24553-9_​68 CrossRef
  12.Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015)
  13.Xie, S., Tu, Z.: Holistically-nested edge detection. In: ICCV (2015)
  14.Zheng, Y., Liu, D., Georgescu, B., Nguyen, H., Comaniciu, D.: 3D deep learning for efficient and robust landmark detection in volumetric data. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9349, pp. 565–572. Springer, Heidelberg (2015). doi:10.​1007/​978-3-319-24553-9_​69 CrossRef
  
• 作者单位：Jameson Merkow (18)
  Alison Marsden (19)
  David Kriegman (18)
  Zhuowen Tu (18)
  
  18. University of California, San Diego, USA
  19. Stanford University, Stanford, USA
  
• 丛书名：Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2016
• ISBN：978-3-319-46726-9
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349
• 卷排序：9902

文摘

In this work, we tackle the important problem of dense 3D volume labeling in medical imaging. We start by introducing HED-3D, a 3D extension of the state-of-the-art 2D edge detector (HED). Next, we develop a novel 3D-Convolutional Neural Network (CNN) architecture, I2I-3D, that predicts boundary location in volumetric data. Our fine-to-fine, deeply supervised framework addresses three critical issues to 3D boundary detection: (1) efficient, holistic, end-to-end volumetric label training and prediction (2) precise voxel-level prediction to capture fine scale structures prevalent in medical data and (3) directed multi-scale, multi-level feature learning. We evaluate our approaches on a dataset consisting of 93 medical image volumes with a wide variety of anatomical regions and vascular structures. We show that our deep learning approaches out-perform the current state-of-the-art in 3D vascular boundary detection (structured forests 3D), by a large margin, as well as HED applied to slices. Prediction takes about one minute on a typical \(512\,\times \,512\,\times \,512\) volume, when using GPU.