TY - JOUR
T1 - Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy
AU - Hu, Haigen
AU - Decazes, Pierre
AU - Vera, Pierre
AU - Li, Hua
AU - Ruan, Su
N1 - Funding Information:
The authors would like to express their appreciation to the referees for their helpful comments and suggestions. This work is co-financed by the European Union with the European regional development fund and by the Normandie Regional Council via the MoNoMaD project (Grant Number: 18P03397/18E01937).
Publisher Copyright:
© 2019, CARS.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - Purpose: Lymphoma detection and segmentation from PET images are critical tasks for cancer staging and treatment monitoring. However, it is still a challenge owing to the complexities of lymphoma PET data themselves, and the huge computational burdens and memory requirements for 3D volume data. In this work, an entropy-based optimization strategy for clustering is proposed to detect and segment lymphomas in 3D PET images. Methods: To reduce computational complexity and add more feature information, billions of voxels in 3D volume data are first aggregated into supervoxels. Then, such supervoxels serve as basic data units for further clustering by using DBSCAN algorithm, in which some new feature attributes based on physical spatial information and prior knowledge are proposed. In addition, more importantly, an entropy-based objective function is constructed to search the most appropriate parameters of DBSCAN to obtain the optimal clustering results by using a genetic algorithm. This step allows to automatically adapt the parameters to each patient. Finally, a series of comparison experiments among various feature attributes are performed. Results: 48 patient data are conducted, showing the combination of three features, supervoxel intensity, geographic coordinates and organ distributions, can achieve good performance and the proposed entropy-based optimization scheme has more advantages than the existing methods. Conclusion: The proposed entropy-based optimization strategy for clustering by integrating physical spatial attributes and prior knowledge can achieve better performance than traditional methods.
AB - Purpose: Lymphoma detection and segmentation from PET images are critical tasks for cancer staging and treatment monitoring. However, it is still a challenge owing to the complexities of lymphoma PET data themselves, and the huge computational burdens and memory requirements for 3D volume data. In this work, an entropy-based optimization strategy for clustering is proposed to detect and segment lymphomas in 3D PET images. Methods: To reduce computational complexity and add more feature information, billions of voxels in 3D volume data are first aggregated into supervoxels. Then, such supervoxels serve as basic data units for further clustering by using DBSCAN algorithm, in which some new feature attributes based on physical spatial information and prior knowledge are proposed. In addition, more importantly, an entropy-based objective function is constructed to search the most appropriate parameters of DBSCAN to obtain the optimal clustering results by using a genetic algorithm. This step allows to automatically adapt the parameters to each patient. Finally, a series of comparison experiments among various feature attributes are performed. Results: 48 patient data are conducted, showing the combination of three features, supervoxel intensity, geographic coordinates and organ distributions, can achieve good performance and the proposed entropy-based optimization scheme has more advantages than the existing methods. Conclusion: The proposed entropy-based optimization strategy for clustering by integrating physical spatial attributes and prior knowledge can achieve better performance than traditional methods.
KW - 3D PET image
KW - Clustering
KW - Entropy-based optimization
KW - Lymphomas detection and segmentation
KW - Supervoxel segmentation
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U2 - 10.1007/s11548-019-02049-2
DO - 10.1007/s11548-019-02049-2
M3 - Article
C2 - 31401714
AN - SCOPUS:85070405256
SN - 1861-6410
VL - 14
SP - 1715
EP - 1724
JO - International Journal of Computer Assisted Radiology and Surgery
JF - International Journal of Computer Assisted Radiology and Surgery
IS - 10
ER -