  Sequential whole-body 18F-Fluorodeoxyglucose (FDG) positron emission
tomography (PET) scans are regarded as the imaging modality of choice for the
assessment of treatment response in the lymphomas because they detect treatment
response when there may not be changes on anatomical imaging. Any computerized
analysis of lymphomas in whole-body PET requires automatic segmentation of the
studies so that sites of disease can be quantitatively monitored over time.
State-of-the-art PET image segmentation methods are based on convolutional
neural networks (CNNs) given their ability to leverage annotated datasets to
derive high-level features about the disease process. Such methods, however,
focus on PET images from a single time-point and discard information from other
scans or are targeted towards specific organs and cannot cater for the multiple
structures in whole-body PET images. In this study, we propose a
spatio-temporal 'dual-stream' neural network (ST-DSNN) to segment sequential
whole-body PET scans. Our ST-DSNN learns and accumulates image features from
the PET images done over time. The accumulated image features are used to
enhance the organs / structures that are consistent over time to allow easier
identification of sites of active lymphoma. Our results show that our method
outperforms the state-of-the-art PET image segmentation methods.
