TY - GEN
T1 - Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images
AU - Lian, Chunfeng
AU - Ruan, Su
AU - Denoux, Thierry
AU - Guo, Yu
AU - Vera, Pierre
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - While hybrid PET/CT scanner is becoming a standard imaging technique in clinical oncology, many existing methods still segment tumor in mono-modality without consideration of complementary information from another modality. In this paper, we propose an unsupervised 3-D method to automatically segment tumor in PET images, where anatomical knowledge from CT images is included as critical guidance to improve PET segmentation accuracy. To this end, a specific context term is proposed to iteratively quantify the conflicts between PET and CT segmentation. In addition, to comprehensively characterize image voxels for reliable segmentation, informative image features are effectively selected via an unsupervised metric learning strategy. The proposed method is based on the theory of belief functions, a powerful tool for information fusion and uncertain reasoning. Its performance has been well evaluated by real-patient PET/CT images.
AB - While hybrid PET/CT scanner is becoming a standard imaging technique in clinical oncology, many existing methods still segment tumor in mono-modality without consideration of complementary information from another modality. In this paper, we propose an unsupervised 3-D method to automatically segment tumor in PET images, where anatomical knowledge from CT images is included as critical guidance to improve PET segmentation accuracy. To this end, a specific context term is proposed to iteratively quantify the conflicts between PET and CT segmentation. In addition, to comprehensively characterize image voxels for reliable segmentation, informative image features are effectively selected via an unsupervised metric learning strategy. The proposed method is based on the theory of belief functions, a powerful tool for information fusion and uncertain reasoning. Its performance has been well evaluated by real-patient PET/CT images.
KW - Belief Functions
KW - Information Fusion
KW - Metric Learning
KW - PET/CT Image Segmentation
KW - Unsupervised Learning
UR - https://www.scopus.com/pages/publications/85045306537
U2 - 10.1109/ICIP.2017.8297123
DO - 10.1109/ICIP.2017.8297123
M3 - 会议稿件
AN - SCOPUS:85045306537
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 4447
EP - 4451
BT - 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
PB - IEEE Computer Society
T2 - 24th IEEE International Conference on Image Processing, ICIP 2017
Y2 - 17 September 2017 through 20 September 2017
ER -