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Tumor segmentation via multi-modality joint dictionary learning

Conference Paper


Abstract


  • © 2018 IEEE. Accurate segmentation of head-and-neck tumor from medical images is crucial for diagnosis and treatment in clinical field. Compared with other types of tumor, the nasopharyngeal carcinoma (NPC) tumor has more complex anatomical structure and often shares similar imaging intensities with the nearby tissues such as brainstem, parotid and lymph, making the segmentation of NPC tumor particularly difficult. In this paper, to take advantage of multi-modality medical information, we propose a multi-modality joint dictionary learning method for NPC tumor segmentation. The tumor segmentation task is formulated as a voxel-wise labeling problem with regard to two classes: NPC tumor and normal tissues. In our method, both the multi-modality samples with CT and MRI images as well as the single-modality samples with only CT or MRI images are effectively utilized to perform joint dictionary learning. Experimental results show that our proposed method outperforms the benchmark method and achieves comparable results with prior NPC segmentation methods.

Authors


  •   Wang, Yan
  •   Yu, Biting (external author)
  •   Wang, Lei
  •   Zu, Chen (external author)
  •   Luo, Yong (external author)
  •   Wu, Xi (external author)
  •   Yang, Zhipeng (external author)
  •   Zhou, Jiliu (external author)
  •   Zhou, Luping

Publication Date


  • 2018

Citation


  • Wang, Y., Yu, B., Wang, L., Zu, C., Luo, Y., Wu, X., Yang, Z., Zhou, J. & Zhou, L. (2018). Tumor segmentation via multi-modality joint dictionary learning. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1336-1339).

Scopus Eid


  • 2-s2.0-85048095478

Start Page


  • 1336

End Page


  • 1339

Abstract


  • © 2018 IEEE. Accurate segmentation of head-and-neck tumor from medical images is crucial for diagnosis and treatment in clinical field. Compared with other types of tumor, the nasopharyngeal carcinoma (NPC) tumor has more complex anatomical structure and often shares similar imaging intensities with the nearby tissues such as brainstem, parotid and lymph, making the segmentation of NPC tumor particularly difficult. In this paper, to take advantage of multi-modality medical information, we propose a multi-modality joint dictionary learning method for NPC tumor segmentation. The tumor segmentation task is formulated as a voxel-wise labeling problem with regard to two classes: NPC tumor and normal tissues. In our method, both the multi-modality samples with CT and MRI images as well as the single-modality samples with only CT or MRI images are effectively utilized to perform joint dictionary learning. Experimental results show that our proposed method outperforms the benchmark method and achieves comparable results with prior NPC segmentation methods.

Authors


  •   Wang, Yan
  •   Yu, Biting (external author)
  •   Wang, Lei
  •   Zu, Chen (external author)
  •   Luo, Yong (external author)
  •   Wu, Xi (external author)
  •   Yang, Zhipeng (external author)
  •   Zhou, Jiliu (external author)
  •   Zhou, Luping

Publication Date


  • 2018

Citation


  • Wang, Y., Yu, B., Wang, L., Zu, C., Luo, Y., Wu, X., Yang, Z., Zhou, J. & Zhou, L. (2018). Tumor segmentation via multi-modality joint dictionary learning. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (pp. 1336-1339).

Scopus Eid


  • 2-s2.0-85048095478

Start Page


  • 1336

End Page


  • 1339