Fully-Automated Identification of Brain Abnormality From Whole Body FDG PET Imaging by Deep-Learning Based Brain Extraction

Background: The whole brain is often covered in [ 18 F]Fluorodeoxyglucose Positron emission tomography ([ 18 F]FDG-PET) in oncology patients, but the covered brain abnormality is typically screened by visual interpretation without quantitative analysis in clinical practice. In this study, we aimed to develop a fully automated quantitative interpretation pipeline of brain volume from an oncology PET image. Method: We retrospectively collected five hundred oncologic [ 18 F]FDG-PET scans for training and validation of the automated brain extractor. We trained the model for extracting brain volume with two manually drawn bounding boxes on maximal intensity projection (MIP) images. ResNet-50, a convolutional neural network (CNN) was used for the model training. The brain volume was automatically extracted using the CNN model and spatially normalized. As an application of this automated analytic method, we enrolled twenty-four subjects with small cell lung cancer (SCLC) and performed voxelwise two-sample T-test for automatic detection of metastatic lesions. Result: The deep learning-based brain extractor successfully identified the existence of whole-brain volume, with the accuracy of 98% for the validation set. The performance of extracting the brain measured by the intersection-over-union (IOU) of 3-D bounding boxes was 72.9±12.5% for the validation set. As an example of the application to automatically identify brain abnormality, this approach successfully identified the metastatic lesions in three of the four cases of SCLC patients with brain metastasis. Conclusion: Based on the deep-learning based model, the brain volume was successfully extracted from whole-body FDG PET. We suggest this fully automated approach could be used for the quantitative analysis of brain metabolic pattern to identify abnormality during clinical interpretation of oncologic PET studies.