Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

Sergei V. Fotin; Yin Yin; Senthil Periaswamy; Justin Kunz; Hrishikesh Haldankar; Naira Muradyan; François Cornud; Baris Turkbey; Peter L. Choyke

doi:10.1117/12.911620

14 February 2012 Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images

Sergei V. Fotin, Yin Yin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, François Cornud, Baris Turkbey, Peter L. Choyke

Author Affiliations +

Proceedings Volume 8314, Medical Imaging 2012: Image Processing; 83140V (2012) https://doi.org/10.1117/12.911620
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

Fully automated prostate segmentation helps to address several problems in prostate cancer diagnosis and treatment: it can assist in objective evaluation of multiparametric MR imagery, provides a prostate contour for MR-ultrasound (or CT) image fusion for computer-assisted image-guided biopsy or therapy planning, may facilitate reporting and enables direct prostate volume calculation. Among the challenges in automated analysis of MR images of the prostate are the variations of overall image intensities across scanners, the presence of nonuniform multiplicative bias field within scans and differences in acquisition setup. Furthermore, images acquired with the presence of an endorectal coil suffer from localized high-intensity artifacts at the posterior part of the prostate. In this work, a three-dimensional method for fast automated prostate detection based on normalized gradient fields cross-correlation, insensitive to intensity variations and coil-induced artifacts, is presented and evaluated. The components of the method, offline template learning and the localization algorithm, are described in detail. The method was validated on a dataset of 522 T2-weighted MR images acquired at the National Cancer Institute, USA that was split in two halves for development and testing. In addition, second dataset of 29 MR exams from Centre d'Imagerie Médicale Tourville, France were used to test the algorithm. The 95% confidence intervals for the mean Euclidean distance between automatically and manually identified prostate centroids were 4.06 ± 0.33 mm and 3.10 ± 0.43 mm for the first and second test datasets respectively. Moreover, the algorithm provided the centroid within the true prostate volume in 100% of images from both datasets. Obtained results demonstrate high utility of the detection method for a fully automated prostate segmentation.

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Sergei V. Fotin, Yin Yin, Senthil Periaswamy, Justin Kunz, Hrishikesh Haldankar, Naira Muradyan, François Cornud, Baris Turkbey, and Peter L. Choyke "Normalized gradient fields cross-correlation for automated detection of prostate in magnetic resonance images", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140V (14 February 2012); https://doi.org/10.1117/12.911620

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