Tumor segmentation via multi-modality joint dictionary learning

Authors

Yan Wang, Sichuan UniversityFollow
Biting Yu, University of WollongongFollow
Lei Wang, University of WollongongFollow
Chen Zu, Nanjing University of Aeronautics and Astronautics
Yong Luo, West China Hospital
Xi Wu, Chengdu University of Technology
Zhipeng Yang, Chengdu University of Information Technology
Jiliu Zhou, Chengdu University of Technology
Luping Zhou, University of WollongongFollow

RIS ID

128359

Publication Details

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).

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.