Evaluation of different atlas selection strategies for multi-atlas segmentation of low-dose computed tomographic images of whole-body positron emission tomography/computed tomography

Background and Objectives: The increasing clinical use of torso positron emission tomography/computed tomography (PET/CT) demands automated segmentation of torso organs from PET/CT images. We attempt to use the multi-atlas segmentation approach for trunk organ segmentation from the low-dose CT images of PET/CT. Since atlas selection is a prerequisite step for multi-atlas segmentation, this study focuses on evaluating the performance of different atlas selection strategies for torso organ segmentation. Methods: We evaluated two criteria for atlas selection, including image similarity and body mass index (BMI) difference between the atlas and the target image. Based on the two criteria, ten atlases are selected and registered to the target image, followed by the label fusion step to achieve final segmentation. Results: The BMI criterion yields comparable segmentation accuracy to the image similarity criterion but with much less computation time. All the evaluated atlas selection methods have Dice >0.9 for the lungs, heart, and liver and Dice