A survey of prostate modeling for image analysis

Chilali, Ouardia; Ouzzane, A.; Diaf, Moussa; Betrouni, Nacim

doi:10.1016/j.compbiomed.2014.07.019

Cited by 12 publications

(4 citation statements)

References 135 publications

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“…In this section, only the segmentation methods used in CAD for CaP systems are presented and summarized in Table 4, and are mostly intensity based. An exhaustive review of prostate segmentation methods in MRI can be found in [133] as well as prostate modelling in [134].…”

Section: Segmentationmentioning

confidence: 99%

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Lemaître¹,

Martí²,

Freixenet³

et al. 2015

Computers in Biology and Medicine

255

180

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Prostate cancer is the second most diagnosed cancer of men all over the world. In the last few decades, new imaging techniques based on Magnetic Resonance Imaging (MRI) have been developed to improve diagnosis. In practise, diagnosis can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. In this regard, computer-aided detection and computer-aided diagnosis systems have been designed to help radiologists in their clinical practice. Research on computer-aided systems specifically focused for prostate cancer is a young technology and has been part of a dynamic field of research for the last 10 years. This survey aims to provide a comprehensive review of the state-of-the-art in this lapse of time, focusing on the different stages composing the work-flow of a computer-aided system. We also provide a comparison between studies and a discussion about the potential avenues for future research. In addition, this paper presents a new public online dataset which is made available to the research community with the aim of providing a common evaluation framework to overcome some of the current limitations identified in this survey.

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Section: Segmentationmentioning

confidence: 99%

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Lemaître¹,

Martí²,

Freixenet³

et al. 2015

Computers in Biology and Medicine

255

180

View full text Add to dashboard Cite

show abstract

“…Manual detection and segmentation of both prostate gland and prostate carcinoma on multispectral MRI is a time-consuming task, which has to be performed by experienced radiologists [245]. In fact, in addition to conventional structural T1w and T2w MRI protocols, complementary and powerful functional information about the tumor can be extracted [104,213,355] from: DCE [174], DWI [240], and MRSI [197,393]. A standardized interpretation of multiparametric MRI is very difficult and significant inter-observer variability has been reported in the literature [507].…”

Section: Prostate Mri Analysismentioning

confidence: 99%

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Rundo

Tangherloni

Nobile

et al. 2019

Expert Systems with Applications

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Medical imaging systems often require the application of image enhancement techniques to help physicians in anomaly/abnormality detection and diagnosis, as well as to improve the quality of images that undergo automated image processing. In this work we introduce MedGA, a novel image enhancement method based on Genetic Algorithms that is able to improve the appearance and the visual quality of images characterized by a bimodal gray level intensity histogram, by strengthening their two underlying sub-distributions. MedGA can be exploited as a pre-processing step for the enhancement of images with a nearly bimodal histogram distribution, to improve the results achieved by downstream image processing techniques. As a case study, we use MedGA as a clinical expert system for contrast-enhanced Magnetic Resonance image analysis, considering Magnetic Resonance guided Focused Ultrasound Surgery for uterine fibroids. The performances of MedGA are quantitatively evaluated by means of various image enhancement metrics, and compared against the conventional state-of-the-art image enhancement techniques, namely, histogram

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“…Manual detection and segmentation of both prostate gland and prostate carcinoma on multispectral MRI is a time-consuming task, which has to be performed by experienced radiologists [9]. In fact, in addition to conventional structural T1-weighted (T1w) and T2-weighted (T2w) MRI protocols, complementary and powerful functional information about the tumor can be extracted [10][11][12] from: Dynamic Contrast Enhanced (DCE) [13], Diffusion Weighted Imaging (DWI) [14], and Magnetic Resonance Spectroscopic Imaging (MRSI) [15,16]. A standardized interpretation of multiparametric MRI is very difficult and significant inter-observer variability has been reported in the literature [17].…”

Section: Introductionmentioning

confidence: 99%

Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

et al. 2017

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Prostate imaging analysis is difficult in diagnosis, therapy, and staging of prostate cancer. In clinical practice, Magnetic Resonance Imaging (MRI) is increasingly used thanks to its morphologic and functional capabilities. However, manual detection and delineation of prostate gland on multispectral MRI data is currently a time-expensive and operator-dependent procedure. Efficient computer-assisted segmentation approaches are not yet able to address these issues, but rather have the potential to do so. In this paper, a novel automatic prostate MR image segmentation method based on the Fuzzy C-Means (FCM) clustering algorithm, which enables multispectral T1-weighted (T1w) and T2-weighted (T2w) MRI anatomical data processing, is proposed. This approach, using an unsupervised Machine Learning technique, helps to segment the prostate gland effectively. A total of 21 patients with suspicion of prostate cancer were enrolled in this study. Volume-based metrics, spatial overlap-based metrics and spatial distance-based metrics were used to quantitatively evaluate the accuracy of the obtained segmentation results with respect to the gold-standard boundaries delineated manually by an expert radiologist. The proposed multispectral segmentation method was compared with the same processing pipeline applied on either T2w or T1w MR images alone. The multispectral approach considerably outperforms the monoparametric ones, achieving an average Dice Similarity Coefficient 90.77 ± 1.75, with respect to 81.90 ± 6.49 and 82.55 ± 4.93 by processing T2w and T1w imaging alone, respectively. Combining T2w and T1w MR image structural information significantly enhances prostate gland segmentation by exploiting the uniform gray appearance of the prostate on T1w MRI.

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A survey of prostate modeling for image analysis

Cited by 12 publications

References 135 publications

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

Computer-Aided Detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

MedGA: A novel evolutionary method for image enhancement in medical imaging systems

Automated Prostate Gland Segmentation Based on an Unsupervised Fuzzy C-Means Clustering Technique Using Multispectral T1w and T2w MR Imaging

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