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Automated medical image segmentation is an essential task to aid/speed updiagnosis and treatment procedures in clinical practices. Deep convolutionalneural networks have exhibited promising performance in accurate and automaticseminal segmentation. For segmentation tasks, these methods normally rely onminimizing a cost/loss function that is designed to maximize the overlapbetween the estimated target and the ground-truth mask delineated by theexperts. A simple loss function based on the degrees of overlap (i.e., Dicemetric) would not take into account the underlying shape and morphology of thetarget subject, as well as its realistic/natural variations; therefore,suboptimal segmentation results would be observed in the form of islands ofvoxels, holes, and unrealistic shapes or deformations. In this light, manystudies have been conducted to refine/post-process the segmentation outcome andconsider an initial guess as prior knowledge to avoid outliers and/orunrealistic estimations. In this study, a novel shape-based cost function isproposed which encourages/constrains the network to learn/capture theunderlying shape features in order to generate a valid/realistic estimation ofthe target structure. To this end, the Principal Component Analysis (PCA) wasperformed on a vectorized training dataset to extract eigenvalues andeigenvectors of the target subjects. The key idea was to use the reconstructionweights to discriminate valid outcomes from outliers/erroneous estimations.

A novel shape-based loss function for machine learning-based seminal organ segmentation in medical imaging