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Proceedings Paper

Linked statistical shape models for multi-modal segmentation of the prostate on MRI-CT for radiotherapy planning
Author(s): Najeeb Chowdhury; Jonathan Chappelow; Robert Toth; Sung Kim; Stephen Hahn; Neha Vapiwala; Haibo Lin; Stefan Both; Anant Madabhushi
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Paper Abstract

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.

Paper Details

Date Published: 15 March 2011
PDF: 15 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 796314 (15 March 2011); doi: 10.1117/12.878416
Show Author Affiliations
Najeeb Chowdhury, Rutgers, The State Univ. of New Jersey (United States)
Jonathan Chappelow, Rutgers, The State Univ. of New Jersey (United States)
Robert Toth, Rutgers, The State Univ. of New Jersey (United States)
Sung Kim, Robert Wood Johnson Univ. Hospital (United States)
Stephen Hahn, Hospital of the Univ. of Pennsylvania (United States)
Neha Vapiwala, Hospital of the Univ. of Pennsylvania (United States)
Haibo Lin, Hospital of the Univ. of Pennsylvania (United States)
Stefan Both, Hospital of the Univ. of Pennsylvania (United States)
Anant Madabhushi, Rutgers, The State Univ. of New Jersey (United States)

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers M.D.; Bram van Ginneken, Editor(s)

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