  Medical image slice interpolation is an active field of research. The methods
for this task can be categorized into two broad groups: intensity-based and
object-based interpolation methods. While intensity-based methods are generally
easier to perform and less computationally expensive, object-based methods are
capable of producing more accurate results and account for deformable changes
in the objects within the slices. In this paper, performance of two well-known
object-based interpolation methods is analyzed and compared. Here, a deformable
registration-based method specifically designed for medical applications and a
learning-based method, trained for video frame interpolation, are considered.
While the deformable registration-based technique is capable of accurate
modeling of the changes in the shapes of the objects within slices, the
learning-based method is able to produce results with similar accuracy, but
with a much sharper appearance in a fraction of the time. This is despite the
fact that the learning-based approach is not trained on medical images and
rather is trained using regular video footage. However, experiments show that
the method is capable of accurate slice interpolation results.
