Physics-Augmented Surface Signed Distance Field Network (PA-SSDF) for Liver Segmentation

Liver segmentation is a challenging task for various clinical applications. The challenges include a weak boundary between the liver and its neighbors, speckle noise, partial volume effect, and large variations in both the shape and size of patient’s organs. The traditional deep learning models do not incorporate the biomechanical and physical characteristics of organs, and hence they may produce inaccurate shape deformation, which is useful much in clinical circumstances. Hence, this research work tackles the above problem using a physics-enhanced approach to detect the boundaries. The proposed model achieved a Dice score of 0.96, a 95th percentile Hausdorff distance of 2.9, an average surface distance of 0.65, an accuracy of 98.1%, a precision of 97.6%, a recall of 97.2% and an F1-score of 97.4%.