Towards a universal MRI atlas of the prostate and prostate zones : Comparison of MRI vendor and image acquisition parameters

The aim of this study was to evaluate an automatic multi-atlas-based segmentation method for generating prostate, peripheral (PZ), and transition zone (TZ) contours on MRIs with and without fat saturation (±FS), and compare MRIs from different vendor MRI systems.

T2-weighted (T2) and fat-saturated (T2FS) MRIs were acquired on 3T GE (GE, Waukesha, WI, USA) and Siemens (Erlangen, Germany) systems. Manual prostate and PZ contours were used to create atlas libraries. As a test MRI is entered, the procedure for atlas segmentation automatically identifies the atlas subjects that best match the test subject, followed by a normalized intensity-based free-form deformable registration. The contours are transformed to the test subject, and Dice similarity coefficients (DSC) and Hausdorff distances between atlas-generated and manual contours were used to assess performance.

Three atlases were generated based on GE_T2 (n = 30), GE_T2FS (n = 30), and Siem_T2FS (n = 31). When test images matched the contrast and vendor of the atlas, DSCs of 0.81 and 0.83 for T2 ± FS were obtained (baseline performance). Atlases performed with higher accuracy when segmenting (i) T2FS vs. T2 images, likely due to a superior contrast between prostate vs. surrounding tissue; (ii) prostate vs. zonal anatomy; (iii) in the mid-gland vs. base and apex. Atlases performance declined when tested with images with differing contrast and MRI vendor. Conversely, combined atlases showed similar performance to baseline.

The MRI atlas-based segmentation method achieved good results for prostate, PZ, and TZ compared to expert contoured volumes. Combined atlases performed similarly to matching atlas and scan type. The technique is fast, fully automatic, and implemented on commercially available clinical platform.

Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al]. 2018 Aug 23 [Epub ahead of print]

Kyle R Padgett, Amy Swallen, Sara Pirozzi, Jon Piper, Felix M Chinea, Matthew C Abramowitz, Aaron Nelson, Alan Pollack, Radka Stoyanova

Department of Radiology, Miller School of Medicine University of Miami, Miami, FL, USA., Research and Development, MIM software Inc., Cleveland, OH, USA., Department of Radiation Oncology, Miller School of Medicine University of Miami, 1475 NW 12th Avenue, Suite 1515J, 33136, Miami, FL, USA. .