  Effective training of deep image segmentation models is challenging due to
the need for abundant, high-quality annotations. Generating annotations is
laborious and time-consuming for human experts, especially in medical image
segmentation. To facilitate image annotation, we introduce Physics Informed
Contour Selection (PICS) - an interpretable, physics-informed algorithm for
rapid image segmentation without relying on labeled data. PICS draws
inspiration from physics-informed neural networks (PINNs) and an active contour
model called snake. It is fast and computationally lightweight because it
employs cubic splines instead of a deep neural network as a basis function. Its
training parameters are physically interpretable because they directly
represent control knots of the segmentation curve. Traditional snakes involve
minimization of the edge-based loss functionals by deriving the Euler-Lagrange
equation followed by its numerical solution. However, PICS directly minimizes
the loss functional, bypassing the Euler Lagrange equations. It is the first
snake variant to minimize a region-based loss function instead of traditional
edge-based loss functions. PICS uniquely models the three-dimensional (3D)
segmentation process with an unsteady partial differential equation (PDE),
which allows accelerated segmentation via transfer learning. To demonstrate its
effectiveness, we apply PICS for 3D segmentation of the left ventricle on a
publicly available cardiac dataset. While doing so, we also introduce a new
convexity-preserving loss term that encodes the shape information of the left
ventricle to enhance PICS's segmentation quality. Overall, PICS presents
several novelties in network architecture, transfer learning, and
physics-inspired losses for image segmentation, thereby showing promising
outcomes and potential for further refinement.
