Deep image-to-image recurrent network with shape basis learning for automatic vertebra labeling in large-scale 3D CT volumes

Dong Yang, Tao Xiong, Daguang Xu, S. Kevin Zhou, Zhoubing Xu, Mingqing Chen, Jin Hyeong Park, Sasa Grbic, Trac D. Tran, Sang Peter Chin, Dimitris Metaxas, Dorin Comaniciu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

15 Scopus citations

Abstract

Automatic vertebra localization and identification in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postoperative assessment. In this paper, we propose an automatic and efficient algorithm to localize and label the vertebra centroids in 3D CT volumes. First, a deep image-to-image network (DI2IN) is deployed to initialize vertebra locations, employing the convolutional encoder-decoder architecture. Next, the centroid probability maps from DI2IN are modeled as a sequence according to the spatial relationship of vertebrae, and evolved with the convolutional long short-term memory (ConvLSTM) model. Finally, the landmark positions are further refined and regularized by another neural network with a learned shape basis. The whole pipeline can be conducted in the end-to-end manner. The proposed method outperforms other state-of-the-art methods on a public database of 302 spine CT volumes with various pathologies. To further boost the performance and validate that large labeled training data can benefit the deep learning algorithms, we leverage the knowledge of additional 1000 3D CT volumes from different patients. Our experimental results show that training with a large database improves the performance of proposed framework by a large margin and achieves an identification rate of 89%.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings
EditorsLena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins
PublisherSpringer Verlag
Pages498-506
Number of pages9
ISBN (Print)9783319661780
DOIs
StatePublished - Jan 1 2017
Event20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 11 2017Sep 13 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10435 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/11/179/13/17

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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    Yang, D., Xiong, T., Xu, D., Zhou, S. K., Xu, Z., Chen, M., Park, J. H., Grbic, S., Tran, T. D., Chin, S. P., Metaxas, D., & Comaniciu, D. (2017). Deep image-to-image recurrent network with shape basis learning for automatic vertebra labeling in large-scale 3D CT volumes. In L. Maier-Hein, A. Franz, P. Jannin, S. Duchesne, M. Descoteaux, & D. L. Collins (Eds.), Medical Image Computing and Computer Assisted Intervention − MICCAI 2017 - 20th International Conference, Proceedings (pp. 498-506). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10435 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66179-7_57