Learning Without Real Data Annotations to Detect Hepatic Lesions in PET Images

Objective:
Deep neural networks have been recently applied to lesion identification in fluorodeoxyglucose (FDG) positron emission tomography (PET) images, but they typically rely on a large amount of well-annotated data for model training. This is extremely difficult to achieve for neuroendocrine tumors (NETs), because of low incidence of NETs and expensive lesion annotation in PET images. The objective of this study is to design a novel, adaptable deep learning method, which uses no real lesion annotations but instead low-cost, list mode-simulated data, for hepatic lesion detection in real-world clinical NET PET images.
Methods:
We first propose a region-guided generative adversarial network (RG-GAN) for lesion-preserved image-to-image translation. Then, we design a specific data augmentation module for our list-mode simulated data and incorporate this module into the RG-GAN to improve model training. Finally, we combine the RG-GAN, the data augmentation module and a lesion detection neural network into a unified framework for joint-task learning to adaptatively identify lesions in real-world PET data.
Results:
The proposed method outperforms recent state-of-the-art lesion detection methods in real clinical
$^{68}$
Ga-DOTATATE PET images, and produces very competitive performance with the target model that is trained with real lesion annotations.
Conclusion:
With RG-GAN modeling and specific data augmentation, we can obtain good lesion detection performance without using any real data annotations.
Significance:
This study introduces an adaptable deep learning method for hepatic lesion identification in NETs, which can significantly reduce human effort for data annotation and improve model generalizability for lesion detection with PET imaging.