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Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes

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Abstract

Accurate and fast segmentation and volume estimation of the prostate gland in magnetic resonance (MR) images are necessary steps in the diagnosis, treatment, and monitoring of prostate cancer. This paper presents an algorithm for the prostate gland volume estimation based on the semi-automated segmentation of individual slices in T2-weighted MR image sequences. The proposed sequential registration-based segmentation (SRS) algorithm, which was inspired by the clinical workflow during medical image contouring, relies on inter-slice image registration and user interaction/correction to segment the prostate gland without the use of an anatomical atlas. It automatically generates contours for each slice using a registration algorithm, provided that the user edits and approves the marking in some previous slices. We conducted comprehensive experiments to measure the performance of the proposed algorithm using three registration methods (i.e., rigid, affine, and nonrigid). Five radiation oncologists participated in the study where they contoured the prostate MR (T2-weighted) images of 15 patients both manually and using the SRS algorithm. Compared to the manual segmentation, on average, the SRS algorithm reduced the contouring time by 62 % (a speedup factor of 2.64×) while maintaining the segmentation accuracy at the same level as the intra-user agreement level (i.e., Dice similarity coefficient of 91 versus 90 %). The proposed algorithm exploits the inter-slice similarity of volumetric MR image series to achieve highly accurate results while significantly reducing the contouring time.

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Notes

  1. The entire image is not used as the ROI because registration usually gives a poor result in this case. Therefore, the ROI is usually limited by the user before the registration starts [5, 6]. SRS was designed specifically for the prostate gland where the cross section of the prostate is small at the base and the apex and it usually becomes larger as we approach the mid-gland region. We used a slightly enlarged ROI because the second slice was more likely to contain a larger portion of the prostate than the first slice when we navigated from the base to the apex. Thus, the second slice might not cover the whole prostate if we use the same ROI. The 30 % ROI enlargement was selected based on the empirical data.

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Acknowledgments

The authors would like to thank FedDev Ontario, Canada, for supporting this research. The authors would also like to thank Segasist Technologies for providing DICOM datasets and experts’ markings.

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Authors and Affiliations

  1. Department of Medical Imaging, University of Toronto, Toronto, ON, Canada

    Farzad Khalvati & Masoom A. Haider

  2. Physical Sciences Platform, Sunnybrook Research Institute, Toronto, ON, Canada

    Farzad Khalvati & Masoom A. Haider

  3. Department of Electrical, Computer and Software Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada

    Aryan Salmanpour & Shahryar Rahnamayan

  4. Centre for Bioengineering and Biotechnology, University of Waterloo, Waterloo, ON, Canada

    H. R. Tizhoosh

Authors
  1. Farzad Khalvati
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  2. Aryan Salmanpour
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  3. Shahryar Rahnamayan
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  4. Masoom A. Haider
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  5. H. R. Tizhoosh
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Correspondence to Farzad Khalvati.

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Khalvati, F., Salmanpour, A., Rahnamayan, S. et al. Sequential Registration-Based Segmentation of the Prostate Gland in MR Image Volumes. J Digit Imaging 29, 254–263 (2016). https://doi.org/10.1007/s10278-015-9844-y

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  • DOI: https://doi.org/10.1007/s10278-015-9844-y

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