Paper Info
Title
Deep Neural Network for Automatic Characterization of Lesions on 68Ga-PSMA PET/CT Images.
Abstract
The emerging PSMA-targeted radionuclide therapy provides an effective method for the treatment of advanced metastatic prostate cancer. To optimize the therapeutic effect and maximize the theranostic benefit, there is a need to identify and quantify target lesions prior to treatment. However, this is extremely challenging considering that a high number of lesions of heterogeneous size and uptake may distribute in a variety of anatomical context with different backgrounds. This study proposes an end-to-end deep neural network to characterize the prostate cancer lesions on PSMA imaging automatically. A Ga-PSMA-11 PET/CT image dataset including 71 patients with metastatic prostate cancer was collected from three medical centres for training and evaluating the proposed network. For proof-of-concept, we focus on the detection of bone and lymph node lesions in the pelvic area suggestive for metastases of prostate cancer. The preliminary test on pelvic area confirms the potential of deep learning methods. Increasing the amount of training data may further enhance the performance of the proposed deep learning method.
Year
DOI
Venue
2019
10.1109/EMBC.2019.8857955
EMBC
Field
DocType
Volume
Training set,Computer vision,PET-CT,Computer science,Image segmentation,Prostate cancer,Artificial intelligence,Radiology,Deep learning,Artificial neural network,Lymph node
Conference
2019
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Yu Zhao1399.68
Andrei Gafita200.34
Giles Tetteh3123.61
Fabian Haupt400.34
Ali Afshar-Oromieh500.34
Bjoern H. Menze6103280.31
Matthias Eiber761.65
Axel Rominger8264.66
Kuangyu Shi9368.12