In-depth understanding of the diaphragm’s anatomy and physiology has been of great interest to the medical community, as it is the most important muscle of the respiratory system. While noncontrast four-dimensional (4-D) computed tomography (CT) imaging provides an interesting opportunity for effective acquisition of anatomical and/or functional information from a single modality, segmenting the diaphragm in such images is very challenging not only because of the diaphragm’s lack of image contrast with its surrounding organs but also because of respiration-induced motion artifacts in 4-D CT images. To account for such limitations, we present an automatic segmentation algorithm, which is based on a priori knowledge of diaphragm anatomy. The novelty of the algorithm lies in using the diaphragm’s easy-to-segment contacting organs—including the lungs, heart, aorta, and ribcage—to guide the diaphragm’s segmentation. Obtained results indicate that average mean distance to the closest point between diaphragms segmented using the proposed technique and corresponding manual segmentation is , which is favorable. An important feature of the proposed technique is that it is the first algorithm to delineate the entire diaphragm. Such delineation facilitates applications, where the diaphragm boundary conditions are required such as biomechanical modeling for in-depth understanding of the diaphragm physiology.