Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI

Özer, Sedat; Langer, Deanna L.; Liu, Xin; Haider, Masoom A.; Kwast, Theodorus H. van der; Evans, Andrew; Yang, Yongyi; Wernick, Miles N.; Yetik, İmam Şamil

doi:10.1118/1.3359459

Cited by 116 publications

(115 citation statements)

References 22 publications

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“…The results were not worse than previous studies using multi-parameter MR including diffusion weighted (DWI) and/or dynamic contrast-enhanced MRI (DCE). In the studies combining T2WI, DWI, and DCE images, values of 0.79 and 0.83 for overall AUC were achieved [24,32]. The results were worse than the results of our study, which only used T2WI.…”

Section: Discussioncontrasting

confidence: 54%

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Zhao

Wang

et al. 2015

Sci. China Life Sci.

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Computer-aided diagnosis (CAD) systems have been proposed to assist radiologists in making diagnostic decisions by providing helpful information. As one of the most important sequences in prostate magnetic resonance imaging (MRI), image features from T2-weighted images (T2WI) were extracted and evaluated for the diagnostic performances by using CAD. We extracted 12 quantitative image features from prostate T2-weighted MR images. The importance of each feature in cancer identification was compared in the peripheral zone (PZ) and central gland (CG), respectively. The performance of the computer-aided diagnosis system supported by an artificial neural network was tested. With computer-aided analysis of T2-weighted images, many characteristic features with different diagnostic capabilities can be extracted. We discovered most of the features (10/12) had significant difference (P<0.01) between PCa and non-PCa in the PZ, while only five features (sum average, minimum value, standard deviation, 10th percentile, and entropy) had significant difference in CG. CAD prediction by features from T2w images can reach high accuracy and specificity while maintaining acceptable sensitivity. The outcome is convictive and helpful in medical diagnosis.prostate cancer, magnetic resonance imaging, T2WI, diagnosis, computer-assisted Citation:

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Section: Discussioncontrasting

confidence: 54%

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Zhao

Wang

et al. 2015

Sci. China Life Sci.

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“…The SVM algorithm is a machine-learning method, which is usually applied for data classification (16). The method had been used for the detection of prostate cancer with MRI and the classification of lymph nodes (17,18). Ozer et al assessed the MRI data of 20 patients with biopsy-proven prostate cancer using different methods for classification analysis.…”

Section: Discussionmentioning

confidence: 99%

Shortened Acquisition Protocols for the Quantitative Assessment of the 2-Tissue-Compartment Model Using Dynamic PET/CT ¹⁸F-FDG Studies

Strauss¹,

Pan²,

Chen³

et al. 2011

J Nucl Med

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18 F-FDG kinetics are quantified by a 2-tissue-compartment model. The routine use of dynamic PET is limited because of this modality's 1-h acquisition time. We evaluated shortened acquisition protocols up to 0-30 min regarding the accuracy for data analysis with the 2-tissue-compartment model. Methods: Full dynamic series for 0-60 min were analyzed using a 2-tissue-compartment model. The time-activity curves and the resulting parameters for the model were stored in a database. Shortened acquisition data were generated from the database using the following time intervals: 0-10, 0-16, 0-20, 0-25, and 0-30 min. Furthermore, the impact of adding a 60-min uptake value to the dynamic series was evaluated. The datasets were analyzed using dedicated software to predict the results of the full dynamic series. The software is based on a modified support vector machines (SVM) algorithm and predicts the compartment parameters of the full dynamic series. Results: The SVM-based software provides user-independent results and was accurate at predicting the compartment parameters of the full dynamic series. If a squared correlation coefficient of 0.8 (corresponding to 80% explained variance of the data) was used as a limit, a shortened acquisition of 0-16 min was accurate at predicting the 60-min 2-tissue-compartment parameters. If a limit of 0.9 (90% explained variance) was used, a dynamic series of at least 0-20 min together with the 60-min uptake values is required. Conclusion: Shortened acquisition protocols can be used to predict the parameters of the 2-tissue-compartment model. Either a dynamic PET series of 0-16 min or a combination of a dynamic PET/CT series of 0-20 min and a 60-min uptake value is accurate for analysis with a 2-tissue-compartment model. Thest andard radiopharmaceutical for PET examinations in oncologic patients is 18 F-FDG, a marker of tumor viability, which has been used with PET for many years now (1). The basic quantitative assessment of the tracer uptake is usually calculated using standardized uptake values (SUVs)-a method introduced by our group more than 19 y ago as a ratio of the local tracer concentration with the injected dose and body volume (2). SUV is a distribution value, which is equal to 1 for a homogeneous distribution of the tracer and exceeds 1 if retention occurs in the tissue. The SUV or, alternatively, the maximum SUV in a volume of interest (VOI) has been used in many publications and was found to be useful as an additional parameter for tumor diagnosis and assessment of therapeutic effects. However, the SUV reflects the global uptake of a tracer and is dependent not only on the specific retention of 18 F-FDG but also on the fractional blood volume and other parameters (3). More detailed information can be obtained by compartment modeling. Vriens et al. reviewed the methodologic aspects of tracer quantification in oncologic patients (4). The authors concluded that the SUV is helpful despite these limitations. Pharmacokinetic quantification is mainly confined to application in a re...

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“…In the clinical practice nowadays three different modalities of MR images are normally produced: T2-weighted, diffusion-weighted and dynamic contrast enhanced images. Recently, many scientific works have proved that MRI has very high accuracy in the detection of prostate diseases [8], [9] significantly improving the diagnostic rates. It enables easier image segmentation and determination of prostate shape and boundaries which is the basic step in clinical applications.…”

Section: Introductionmentioning

confidence: 99%

Topological Prostate Segmentation Method in MRI

Stojanov¹,

Koceski²

2014

Annals of Computer Science and Information Systems

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Abstract-The main aim of this paper is to advance the state of the art in automated prostate segmentation using T2 weighted MR images, by introducing a hybrid topological MRI prostate segmentation method which is based on a set of prelabeled MR atlas images. The proposed method has been experimentally tested on a set of 30 MRI T2 weighted images. For evaluation the automated segmentations of the proposed scheme have been compared with the manual segmentations, using an average Dice Similarity Coefficient (DSC). Obtained quantitative results have shown a good approximation of the segmented prostate.

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Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI

Cited by 116 publications

References 22 publications

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Shortened Acquisition Protocols for the Quantitative Assessment of the 2-Tissue-Compartment Model Using Dynamic PET/CT ¹⁸F-FDG Studies

Topological Prostate Segmentation Method in MRI

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Supervised and unsupervised methods for prostate cancer segmentation with multispectral MRI

Cited by 116 publications

References 22 publications

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Prostate cancer identification: quantitative analysis of T2-weighted MR images based on a back propagation artificial neural network model

Shortened Acquisition Protocols for the Quantitative Assessment of the 2-Tissue-Compartment Model Using Dynamic PET/CT 18F-FDG Studies

Topological Prostate Segmentation Method in MRI

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Shortened Acquisition Protocols for the Quantitative Assessment of the 2-Tissue-Compartment Model Using Dynamic PET/CT ¹⁸F-FDG Studies