TY - JOUR
T1 - MTR-PET
T2 - Multi-temporal resolution PET images for lymphoma segmentation
AU - Pang, Wenbo
AU - Li, Siqi
AU - Jiang, Huiyan
AU - Yao, Yu dong
N1 - Publisher Copyright: © 2023 Elsevier Ltd
PY - 2024/1
Y1 - 2024/1
N2 - Lymphoma segmentation on positron emission tomography/computed tomography (PET/CT) is a challenge in clinical diagnosis. Existing methods commonly locate lymphoma candidates on PET images and removes false positive candidates using CT images. However, the combined use of PET with CT in segmentation is not trivial, either requiring CT-based multi-organ segmentation or PET/CT registration. The registration of PET/CT will introduce information change in images. Therefore, accurate lymphoma segmentation on PET images is of great importance for medical diagnoses. In this paper, we propose a novel idea of lymphoma segmentation on multi-temporal resolution PET (MTR-PET) images. Instead of using CT information, this method investigates differences of lymphoma and other tissues on different temporal resolution PET images. Two related features, metabolic variation and metabolic heterogeneity, are proposed and combined with traditional features to construct a statistical analysis model for removing false lymphoma candidates. To obtain accurate boundary results, CNN networks are used for lymphoma segmentation on the region of interesting (ROI) images. Our proposed method is evaluated on 53 MTR-PET images with a detection sensibility of 0.9953 and a segmentation Dice coefficient of 0.8667. Experiments and results demonstrate that information of MTR-PET images can significantly improve the performance of lymphoma segmentation.
AB - Lymphoma segmentation on positron emission tomography/computed tomography (PET/CT) is a challenge in clinical diagnosis. Existing methods commonly locate lymphoma candidates on PET images and removes false positive candidates using CT images. However, the combined use of PET with CT in segmentation is not trivial, either requiring CT-based multi-organ segmentation or PET/CT registration. The registration of PET/CT will introduce information change in images. Therefore, accurate lymphoma segmentation on PET images is of great importance for medical diagnoses. In this paper, we propose a novel idea of lymphoma segmentation on multi-temporal resolution PET (MTR-PET) images. Instead of using CT information, this method investigates differences of lymphoma and other tissues on different temporal resolution PET images. Two related features, metabolic variation and metabolic heterogeneity, are proposed and combined with traditional features to construct a statistical analysis model for removing false lymphoma candidates. To obtain accurate boundary results, CNN networks are used for lymphoma segmentation on the region of interesting (ROI) images. Our proposed method is evaluated on 53 MTR-PET images with a detection sensibility of 0.9953 and a segmentation Dice coefficient of 0.8667. Experiments and results demonstrate that information of MTR-PET images can significantly improve the performance of lymphoma segmentation.
KW - Deep learning
KW - Features analysis
KW - Lymphoma segmentation
KW - Multi-temporal resolution PET
KW - Tumor detection
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U2 - 10.1016/j.bspc.2023.105529
DO - 10.1016/j.bspc.2023.105529
M3 - Article
SN - 1746-8094
VL - 87
JO - Biomedical Signal Processing and Control
JF - Biomedical Signal Processing and Control
M1 - 105529
ER -