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We present a novel framework for building a linked statistical shape model (LSSM), a statistical shape model
(SSM) that links the shape variation of a structure of interest (SOI) across multiple imaging modalities. This
framework is particularly relevant in scenarios where accurate delineations of a SOI's boundary on one of the
modalities may not be readily available, or difficult to obtain, for training a SSM. We apply the LSSM in the
context of multi-modal prostate segmentation for radiotherapy planning, where we segment the prostate on MRI
and CT simultaneously. Prostate capsule segmentation is a critical step in prostate radiotherapy planning, where
dose plans have to be formulated on CT. Since accurate delineations of the prostate boundary are very difficult
to obtain on CT, pre-treatment MRI is now beginning to be acquired at several medical centers. Delineation of
the prostate on MRI is acknowledged as being significantly simpler to do compared to CT. Hence, our framework
incorporates multi-modal registration of MRI and CT to map 2D boundary delineations of prostate (obtained
from an expert radiation oncologist) on MR training images onto corresponding CT images. The delineations
of the prostate capsule on MRI and CT allows for 3D reconstruction of the prostate shape which facilitates the
building of the LSSM. We acquired 7 MRI-CT patient studies and used the leave-one-out strategy to train and
evaluate our LSSM (fLSSM), built using expert ground truth delineations on MRI and MRI-CT fusion derived
capsule delineations on CT. A unique attribute of our fLSSM is that it does not require expert delineations of
the capsule on CT. In order to perform prostate MRI segmentation using the fLSSM, we employed a regionbased
approach where we deformed the evolving prostate boundary to optimize a mutual information based
cost criterion, which took into account region-based intensity statistics of the image being segmented. The
final prostate segmentation was then transferred onto the CT image using the LSSM. We compared our fLSSM
against another LSSM (xLSSM), where, unlike the fLSSM, expert delineations of the capsule on both MRI and
CT were employed in the model building; xLSSM representing the idealized LSSM. We also compared our fLSSM
against an exclusive CT-based SSM (ctSSM), built from expert delineations of capsule on CT only. Due to the
intensity-driven nature of the segmentation algorithm, the ctSSM was not able segment the prostate. On MRI,
the xLSSM and fLSSM yielded almost identical results. On CT, our results suggest that the fLSSM, while
not dependent on highly accurate delineations of the capsule on CT, yields comparable results to an idealized
LSSM scheme (xLSSM). Hence, the fLSSM provides an accurate alternative to SSMs that require careful SOI
delineations that may be difficult or laborious to obtain, while providing concurrent segmentations of the capsule
on multiple modalities.
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