Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI
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 s...
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| Format: | Article |
| Language: | English |
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Shiraz University of Medical Sciences
2018-03-01
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| Series: | Journal of Biomedical Physics and Engineering |
| Subjects: | |
| Online Access: | http://www.jbpe.org/Journal_OJS/JBPE/index.php/jbpe/article/view/555 |
| _version_ | 1818065687251255296 |
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| author | Navaei Lavasani S. Mostaar A. Ashtiyani M. |
| author_facet | Navaei Lavasani S. Mostaar A. Ashtiyani M. |
| author_sort | Navaei Lavasani S. |
| collection | DOAJ |
| description | 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 |
| first_indexed | 2024-12-10T14:55:51Z |
| format | Article |
| id | doaj.art-786648d406f1467f9488b7a42e69bc40 |
| institution | Directory Open Access Journal |
| issn | 2251-7200 2251-7200 |
| language | English |
| last_indexed | 2024-12-10T14:55:51Z |
| publishDate | 2018-03-01 |
| publisher | Shiraz University of Medical Sciences |
| record_format | Article |
| series | Journal of Biomedical Physics and Engineering |
| spelling | doaj.art-786648d406f1467f9488b7a42e69bc402022-12-22T01:44:19ZengShiraz University of Medical SciencesJournal of Biomedical Physics and Engineering2251-72002251-72002018-03-018110711610.22086/jbpe.v0i0.555Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRINavaei Lavasani S.0Mostaar A.1Ashtiyani M.2Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranDepartment of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranDepartment of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, IranBackground: 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 credibilityhttp://www.jbpe.org/Journal_OJS/JBPE/index.php/jbpe/article/view/555DCE-MRIProstate CancerSemi-quantitative FeatureWavelet Kinetic FeatureSegmentation |
| spellingShingle | Navaei Lavasani S. Mostaar A. Ashtiyani M. Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI Journal of Biomedical Physics and Engineering DCE-MRI Prostate Cancer Semi-quantitative Feature Wavelet Kinetic Feature Segmentation |
| title | Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI |
| title_full | Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI |
| title_fullStr | Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI |
| title_full_unstemmed | Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI |
| title_short | Automatic Prostate Cancer Segmentation Using Kinetic Analysis in Dynamic Contrast-Enhanced MRI |
| title_sort | automatic prostate cancer segmentation using kinetic analysis in dynamic contrast enhanced mri |
| topic | DCE-MRI Prostate Cancer Semi-quantitative Feature Wavelet Kinetic Feature Segmentation |
| url | http://www.jbpe.org/Journal_OJS/JBPE/index.php/jbpe/article/view/555 |
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