Advertisement

A Multiphase Validation of Atlas-Based Automatic and Semiautomatic Segmentation Strategies for Prostate MRI

      Purpose

      To perform a rigorous technological assessment and statistical validation of a software technology for anatomic delineations of the prostate on MRI datasets.

      Methods and Materials

      A 3-phase validation strategy was used. Phase I consisted of anatomic atlas building using 100 prostate cancer MRI data sets to provide training data sets for the segmentation algorithms. In phase II, 2 experts contoured 15 new MRI prostate cancer cases using 3 approaches (manual, N points, and region of interest). In phase III, 5 new physicians with variable MRI prostate contouring experience segmented the same 15 phase II datasets using 3 approaches: manual, N points with no editing, and full autosegmentation with user editing allowed. Statistical analyses for time and accuracy (using Dice similarity coefficient) endpoints used traditional descriptive statistics, analysis of variance, analysis of covariance, and pooled Student t test.

      Results

      In phase I, average (SD) total and per slice contouring time for the 2 physicians was 228 (75), 17 (3.5), 209 (65), and 15 seconds (3.9), respectively. In phase II, statistically significant differences in physician contouring time were observed based on physician, type of contouring, and case sequence. The N points strategy resulted in superior segmentation accuracy when initial autosegmented contours were compared with final contours. In phase III, statistically significant differences in contouring time were observed based on physician, type of contouring, and case sequence again. The average relative timesaving for N points and autosegmentation were 49% and 27%, respectively, compared with manual contouring. The N points and autosegmentation strategies resulted in average Dice values of 0.89 and 0.88, respectively. Pre- and postedited autosegmented contours demonstrated a higher average Dice similarity coefficient of 0.94.

      Conclusion

      The software provided robust contours with minimal editing required. Observed time savings were seen for all physicians irrespective of experience level and baseline manual contouring speed.
      To read this article in full you will need to make a payment
      ASTRO Member Login
      ASTRO Members, full access to the journal is a member benefit. Use your society credentials to access all journal content and features.
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Jameson M.G.
        • Holloway L.C.
        • Vial P.J.
        • et al.
        A review of methods of analysis in contouring studies for radiation oncology.
        J Med Imaging Radiat Oncol. 2010; 54: 401-410
        • Weiss E.
        • Hess C.F.
        The impact of gross tumor volume (GTV) and clinical target volume (CTV) definition on the total accuracy in radiotherapy theoretical aspects and practical experiences.
        Strahlenther Onkol. 2003; 179: 21-30
        • Chen A.
        • Niermann K.J.
        • Deeley M.A.
        • et al.
        Evaluation of multiple-atlas-based strategies for segmentation of the thyroid gland in head and neck CT images for IMRT.
        Phys Med Biol. 2012; 57: 93-111
        • Leung K.K.
        • Barnes J.
        • Modat M.
        • et al.
        Brain MAPS: an automated, accurate and robust brain extraction technique using a template library.
        Neuroimage. 2011; 55: 1091-1108
      1. Prostate MR Image Database. http://prostateMRimageDatabase.com.

        • Stapleford L.J.
        • Lawson J.D.
        • Perkins C.
        • et al.
        Evaluation of automatic atlas-based lymph node segmentation for head-and-neck cancer.
        Int J Radiat Oncol Biol Phys. 2010; 77: 959-966
        • Lester H.
        • Arridge S.R.
        A survey of hierarchical non-linear medical image registration.
        Pattern Recognition. 1999; 32: 129-149
        • Crum W.R.
        • Hartkens T.
        • Hill D.L.
        Non-rigid image registration: theory and practice.
        Br J Radiol. 2004; 77: S140-S153
        • Dice L.R.
        Measures of the amount of ecologic association between species.
        Ecology. 1945; 26: 297-302
        • Louie A.V.
        • Rodrigues G.
        • Olsthoorn J.
        • et al.
        Inter-observer and intra-observer reliability for lung cancer target volume delineation in the 4D-CT era.
        Radiother Oncol. 2010; 95: 166-171
        • Zijdenbos A.P.
        • Dawant B.M.
        • Margolin R.A.
        • et al.
        Morphometric analysis of white matter lesions in MR images: method and validation.
        IEEE Trans Med Imaging. 1994; 13: 716-724
        • Roach 3rd, M.
        • Faillace-Akazawa P.
        • Malfatti C.
        • et al.
        Prostate volumes defined by magnetic resonance imaging and computerized tomographic scans for three-dimensional conformal radiotherapy.
        Int J Radiat Oncol Biol Phys. 1996; 35: 1011-1018
        • Kagawa K.
        • Lee W.R.
        • Schultheiss T.E.
        • et al.
        Initial clinical assessment of CT-MRI image fusion software in localization of the prostate for 3D conformal radiation therapy.
        Int J Radiat Oncol Biol Phys. 1997; 38: 319-325
        • Steenbakkers R.J.
        • Deurloo K.E.
        • Nowak P.J.
        • et al.
        Reduction of dose delivered to the rectum and bulb of the penis using MRI delineation for radiotherapy of the prostate.
        Int J Radiat Oncol Biol Phys. 2003; 57: 1269-1279
        • Makni N.
        • Puech P.
        • Lopes R.
        • et al.
        Automatic 3D segmentation of prostate in MRI combining a priori knowledge, Markov fields and Bayesian framework.
        Conf Proc IEEE Eng Med Biol Soc. 2008; : 2992-2995
        • Klein S.
        • van der Heide U.A.
        • Lips I.M.
        • et al.
        Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information.
        Med Phys. 2008; 35: 1407-1417
        • Njeh C.F.
        Tumor delineation: the weakest link in the search for accuracy in radiotherapy.
        J Med Phys. 2008; 33: 136-140

      Comments

      Commenting Guidelines

      To submit a comment for a journal article, please use the space above and note the following:

      • We will review submitted comments as soon as possible, striving for within two business days.
      • This forum is intended for constructive dialogue. Comments that are commercial or promotional in nature, pertain to specific medical cases, are not relevant to the article for which they have been submitted, or are otherwise inappropriate will not be posted.
      • We require that commenters identify themselves with names and affiliations.
      • Comments must be in compliance with our Terms & Conditions.
      • Comments are not peer-reviewed.