BSCI-14. SYNTHETIC METASTATIC BRAIN DISEASE MRI IMAGES CREATED USING A GENERATIVE ADVERSARY NETWORK TO OVERCOME DEEP MACHINE LEARNING CHALLENGES IN HEALTHCARE

• Published in: Neuro-Oncology Advances
• Main Authors: Dai, Zhenzhen, Snyder, James, Wen, Ning
• Format: Text
• Language: English
• Published: Oxford University Press 2019
• Subjects: Abstracts; DML
• Online Access: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7213370/; https://doi.org/10.1093/noajnl/vdz014.012

Deep Machine Learning (DML) in commercial applications such as recognizing animal species in photographs occurred through analyzing large volumes of public data. To achieve similar success in brain tumor imaging, additional factors must be addressed such as the need to follow strict regulatory protocols, work with limited datasets, and protect patient privacy. Generative adversary network (GAN) restricted to intracranial disease is one possibility to overcome these challenges and enable training on small annotated datasets to synthesize new samples. Large fabricated brain metastases (BM) training datasets derived from patient MRI using GAN models may enable DML of BM MRI studies. METHOD: We randomly selected 82 glioma patient imaging studies from the MICCAI BraTS 2018 Challenge(1). All patients underwent contouring of GD-enhancing tumor (C+), peritumoral T2 (pT2), necrotic and non-enhancing tumor core (NCR/NET). Images are co-registered to the anatomical template and skull-stripped. Our network consists of a GAN and a discriminative network. The generative model works to synthesize images from labels. Labels comprise the normal brain mask as well as the contoured C+, pT2 and NCR/NET. Normal brain mask is extracted from threshold segmentation on T2-weighted image (T2WI). A discriminative network compares the difference between synthetic and real patient image in both pixel and perceptual difference. The generative model is trained to minimize the difference from the discriminative network. This method was refined in the glioblastoma dataset and applied to BM MRI. RESULTS: Figure 1. Synthetic BM MRI images derived from human brain MRI studies using the GAN model with four modalities (T2, T2 FLAIR, T1 contrasted image, and T1 non-contrasted Image). CONCLUSION: Training DML in BM disease using GAN MRI models may overcome limitations in applying DML to healthcare, namely volume of high-quality data and patient privacy. GAN based modeling for BM needs to be further refined and validated.