Challenges and methodologies of fully automatic whole heart segmentation: A review

Abstract

Whole heart segmentation from magnetic resonance imaging or computed tomography is a prerequisite for many clinical applications. Since manual delineation can be tedious and subject to bias, automating such segmentation becomes increasingly popular in the image computing field. However, fully automatic whole heart segmentation is challenging and only limited studies were reported in the literature. This article reviews the existing techniques and analyzes the challenges and methodologies. The techniques are classified in terms of the types of the prior models and the algorithms used to fit the model to unseen images. The prior models include the atlases and the deformable models, and the fitting algorithms are further decomposed into four key techniques including localization of the whole heart, initialization of substructures, refinement of boundary delineation, and regularization of shapes. Finally, the validation issues, challenges, and future directions are discussed.