报告人： Ivica Kopriva, Ph.D, senior scientist, the Ru?erBoškovi? Institute,
邀请人： 陈新建 特聘教授
Recently, novel method for automatic segmentation of lung tumor from 3D PET images was proposed by us (I. Kopriva, et al. IEEE Journal of Biomedical and Health Informatics, doi:10.1109/JBHI.2016.2624798). In particular, we combine feature expansion transform with sparseness constrained nonnegative matrix factorization to decompose 3D PET image into constituent components. Afterwards, by using complexity based criterion, we select tumor component as the one with minimal complexity. The proposed method was compared with threshold based on 40% and 50% maximum standardized uptake value (SUV), graph cuts (GC), random walks (RW) and affinity propagation (AP) algorithms on 18 non-small cell lung cancer datasets with respect to ground truth provided by two radiologists. Dice similarity coefficient averaged with respect to two ground truths is: 0.78±0.12 by the proposed algorithm, 0.78±0.1 by GC, 0.77±0.13 by AP, 0.77±0.07 by RW, and 0.75±0.13 by 50% maximum SUV threshold. Since the proposed method achieved performance comparable with interactive methods, considering the unique challenges of lung tumor segmentation from PET images, our findings support possibility of using our fully automated method in routine clinics.
Ivica Kopriva obtained PhD degree from the Faculty of Electrical Engineering and Computing,