عنوان انگلیسی مقاله:
Deep learning for variational multimodality tumor segmentation in PET/CT
ترجمه فارسی عنوان مقاله:
یادگیری عمیق برای تقسیم تومور چند متغیری تغییرات در PET / CT
Sciencedirect - Elsevier - Neurocomputing, Corrected proof: doi:10:1016/j:neucom:2018:10:099
Laquan Li a , b , Xiangming Zhao a , Wei Lu c , Shan Tan a , ∗
Positron emission tomography/computed tomography (PET/CT) imaging can simultaneously acquire func- tional metabolic information and anatomical information of the human body. How to rationally fuse the complementary information in PET/CT for accurate tumor segmentation is challenging. In this study, a novel deep learning based variational method was proposed to automatically fuse multimodality infor- mation for tumor segmentation in PET/CT. A 3D fully convolutional network (FCN) was first designed and trained to produce a probability map from the CT image. The learnt probability map describes the prob- ability of each CT voxel belonging to the tumor or the background, and roughly distinguishes the tumor from its surrounding soft tissues. A fuzzy variational model was then proposed to incorporate the prob- ability map and the PET intensity image for an accurate multimodality tumor segmentation, where the probability map acted as a membership degree prior. A split Bregman algorithm was used to minimize the variational model. The proposed method was validated on a non-small cell lung cancer dataset with 84 PET/CT images. Experimental results demonstrated that: (1) Only a few training samples were needed for training the designed network to produce the probability map; (2) The proposed method can be ap- plied to small datasets, normally seen in clinic research; (3) The proposed method successfully fused the complementary information in PET/CT, and outperformed two existing deep learning-based multi- modality segmentation methods and other multimodality segmentation methods using traditional fusion strategies (without deep learning); (4) The proposed method had a good performance for tumor segmen- tation, even for those with Fluorodeoxyglucose (FDG) uptake inhomogeneity and blurred tumor edges (two major challenges in PET single modality segmentation) and complex surrounding soft tissues (one major challenge in CT single modality segmentation), and achieved an average dice similarity indexes (DSI) of 0.86 ±0.05, sensitivity (SE) of 0.86 ±0.07, positive predictive value (PPV) of 0.87 ±0.10, volume error (VE) of 0.16 ±0.12, and classification error (CE) of 0.30 ±0.12.
Keywords: Tumor segmentation | PET/CT images | Variational method | Deep learning | Information fusion