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Analyzing the hippocampus in the brain through magnetic resonance imaging (MRI) plays a crucial role in diagnosing and making treatment decisions for several neurological diseases. Hippocampus atrophy is among the most informative early diagnostic biomarkers of Alzheimer's disease (AD), yet its automatic segmentation is extremely difficult given the anatomical structure of the brain and the lack of any contrast in between its different regions. The gold standard remains manual segmentation and the use of brain atlases. In this study, we use a well-known image segmentation model, UNet++, and introduce an attention mechanism called the Convolutional Block Attention Module (CBAM) to the UNet++ model. This integrated model improves the feature weights of our region of interest, and hence increases the accuracy in segmenting the hippocampus. Results show averages of 0.8715, 0.8107, 0.8872, and 0.9039 for the metrics of Dice, Jaccard, Precision, and Recall, respectively. 

The Standard Uptake Value (SUV) is conventionally calculated using the ratio of the injected PET radiotracer dose and subject body weight (Binj) . SUVs are used to obtain SUV ratios (SUVr), an important metric in many Alzheimer's Disease (AD) neuroimaging studies. However, SUVr can be obtained using only neuroimaging data, bypassing the need for Binj . This paper proposes the SUVr-LightWeight (SUVr-LW) algorithm which is not reliant on clinical data and instead focuses on PET intensity values. The SUVr-LW was evaluated using the Centiloid Project Florebetaben (FBB) subject cohort and reached a linear regression slope of 0.98, while the healthy control subjects produced a slope of 0.87.