Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2018 Mar 1;8(1):107-116.
eCollection 2018 Mar.

Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI

S Navaei Lavasani  1 , A Mostaar  1 , M Ashtiyani  1
Affiliations

Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI

S Navaei Lavasani et al. J Biomed Phys Eng. .

Abstract

Background: Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.

Objective: The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI.

Methods: Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth.

Results: Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, specificity when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).

Conclusion: The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility.

Keywords: Prostate Cancer; Segmentation; Semi-quantitative Feature; Wavelet Kinetic Feature; DCE-MRI.

PubMed Disclaimer

Conflict of interest statement

Conflict of interests: None.

Figures

Figure1
Figure1
Relative time intensity curve. The different parameters that were calculated (Max Relative enhancement, TTP, Wash-in, Wash-out and Area under curve (AUC)) are illustrated on a pixel of malignant prostatic tissue.
Figure2
Figure2
Segmentation of DCE-MRI images (Patient 1, 2, 3). (a, d, g) Tumor outlined by a pathologist, (b, e, h) The segmentation result by the Semi-quantitative features, (d, f, i) and by the Wavelet based features.
See this image and copyright information in PMC

References

    1. Stamey TA, Caldwell M, McNeal JE, Nolley R, Hemenez M, Downs J. The prostate specific antigen era in the United States is over for prostate cancer: what happened in the last 20 years? J Urol. 2004;172:1297–301. doi: 10.1097/01.ju.0000139993.51181.5d. - DOI - PubMed
    1. Artan Y, Langer DL, Haider MA, van*der*Kwast TH, Evans AJ, Wernick MN, et al. Prostate cancer segmentation with multispectral MRI using cost-sensitive conditional random fields. 28 June-1 July 2009 . Boston: Biomedical Imaging: From Nano to Macro, 2009 ISBI'09 IEEE International Symposium on; 2009.
    1. Verma S, Turkbey B, Muradyan N, Rajesh A, Cornud F, Haider MA, et al. Overview of dynamic contrast-enhanced MRI in prostate cancer diagnosis and management. AJR Am J Roentgenol. 2012;198:1277–88. doi: 10.2214/AJR.12.8510. - DOI - PMC - PubMed
    1. Mostaar A, Ashtiyani M, Lavasany SN, Rexhepi AH, Kongoli R, Dey A, et al. An Improved Ant Colony Algorithm Optimization for Automated MRI Segmentation Using Probabilistic Atlas. Int J Innov Res Sci Eng. 2015;3:399, 406.
    1. Turkbey B, Bernardo M, Merino MJ, Wood BJ, Pinto PA, Choyke PL. MRI of localized prostate cancer: coming of age in the PSA era. Diagn Interv Radiol. 2012;18:34–45. - PMC - PubMed

LinkOut - more resources