  Organ morphology is a key indicator for prostate disease diagnosis and
prognosis. For instance, In longitudinal study of prostate cancer patients
under active surveillance, the volume, boundary smoothness and their changes
are closely monitored on time-series MR image data. In this paper, we describe
a new framework for forecasting prostate morphological changes, as the ability
to detect such changes earlier than what is currently possible may enable
timely treatment or avoiding unnecessary confirmatory biopsies. In this work,
an efficient feature-based MR image registration is first developed to align
delineated prostate gland capsules to quantify the morphological changes using
the inferred dense displacement fields (DDFs). We then propose to use kernel
density estimation (KDE) of the probability density of the DDF-represented
\textit{future morphology changes}, between current and future time points,
before the future data become available. The KDE utilises a novel distance
function that takes into account morphology, stage-of-progression and
duration-of-change, which are considered factors in such subject-specific
forecasting. We validate the proposed approach on image masks unseen to
registration network training, without using any data acquired at the future
target time points. The experiment results are presented on a longitudinal data
set with 331 images from 73 patients, yielding an average Dice score of 0.865
on a holdout set, between the ground-truth and the image masks warped by the
KDE-predicted-DDFs.
