  Automated medical image segmentation is an essential task to aid/speed up
diagnosis and treatment procedures in clinical practices. Deep convolutional
neural networks have exhibited promising performance in accurate and automatic
seminal segmentation. For segmentation tasks, these methods normally rely on
minimizing a cost/loss function that is designed to maximize the overlap
between the estimated target and the ground-truth mask delineated by the
experts. A simple loss function based on the degrees of overlap (i.e., Dice
metric) would not take into account the underlying shape and morphology of the
target subject, as well as its realistic/natural variations; therefore,
suboptimal segmentation results would be observed in the form of islands of
voxels, holes, and unrealistic shapes or deformations. In this light, many
studies have been conducted to refine/post-process the segmentation outcome and
consider an initial guess as prior knowledge to avoid outliers and/or
unrealistic estimations. In this study, a novel shape-based cost function is
proposed which encourages/constrains the network to learn/capture the
underlying shape features in order to generate a valid/realistic estimation of
the target structure. To this end, the Principal Component Analysis (PCA) was
performed on a vectorized training dataset to extract eigenvalues and
eigenvectors of the target subjects. The key idea was to use the reconstruction
weights to discriminate valid outcomes from outliers/erroneous estimations.
