Drug-metabolism-based Automatic Segmentation of Dynamic Scintigraphic Images

Quantitative dynamic scintigraphic image studies provide in vivo measurements of dynamic physiological and biochemical processes in humans. Segmentation of scintigraphic images is one of the most important steps to distinguish organs of interest from the background. However, poor spatial resolution and signal-to-noise ratio (SNR) of scintigraphic images make accurate organ boundary segmentation difficult. In scintigraphic image studies, areas of specific binding are usually segmented by manually drawing regions of interest (ROIs), which is a time-consuming and subjective process. In this work, we describe a method to automatically segment 99mTc-GSA dynamic scintigraphic images based on the prior knowledge of drug metabolism, including the drug flow mechanism and the time of activity curves (TACs). Our results suggest that drug-metabolism-based segmentation can automatically segment tissues in dynamic scintigraphic images studies and has the potential to replace manual ROI segmentation for our application.