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Jiang M, Yuan B, Kou W, Yan W, Marshall H, Yang Q, Syer T, Punwani S, Emberton M, Barratt DC, Cho CCM, Hu Y, Chiu B. Prostate cancer segmentation from MRI by a multistream fusion encoder. Med Phys 2023; 50:5489-5504. [PMID: 36938883 DOI: 10.1002/mp.16374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 02/15/2023] [Accepted: 03/03/2023] [Indexed: 03/21/2023] Open
Abstract
BACKGROUND Targeted prostate biopsy guided by multiparametric magnetic resonance imaging (mpMRI) detects more clinically significant lesions than conventional systemic biopsy. Lesion segmentation is required for planning MRI-targeted biopsies. The requirement for integrating image features available in T2-weighted and diffusion-weighted images poses a challenge in prostate lesion segmentation from mpMRI. PURPOSE A flexible and efficient multistream fusion encoder is proposed in this work to facilitate the multiscale fusion of features from multiple imaging streams. A patch-based loss function is introduced to improve the accuracy in segmenting small lesions. METHODS The proposed multistream encoder fuses features extracted in the three imaging streams at each layer of the network, thereby allowing improved feature maps to propagate downstream and benefit segmentation performance. The fusion is achieved through a spatial attention map generated by optimally weighting the contribution of the convolution outputs from each stream. This design provides flexibility for the network to highlight image modalities according to their relative influence on the segmentation performance. The encoder also performs multiscale integration by highlighting the input feature maps (low-level features) with the spatial attention maps generated from convolution outputs (high-level features). The Dice similarity coefficient (DSC), serving as a cost function, is less sensitive to incorrect segmentation for small lesions. We address this issue by introducing a patch-based loss function that provides an average of the DSCs obtained from local image patches. This local average DSC is equally sensitive to large and small lesions, as the patch-based DSCs associated with small and large lesions have equal weights in this average DSC. RESULTS The framework was evaluated in 931 sets of images acquired in several clinical studies at two centers in Hong Kong and the United Kingdom. In particular, the training, validation, and test sets contain 615, 144, and 172 sets of images, respectively. The proposed framework outperformed single-stream networks and three recently proposed multistream networks, attaining F1 scores of 82.2 and 87.6% in the lesion and patient levels, respectively. The average inference time for an axial image was 11.8 ms. CONCLUSION The accuracy and efficiency afforded by the proposed framework would accelerate the MRI interpretation workflow of MRI-targeted biopsy and focal therapies.
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Affiliation(s)
- Mingjie Jiang
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Baohua Yuan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
- Aliyun School of Big Data, Changzhou University, Changzhou, China
| | - Weixuan Kou
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
| | - Wen Yan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
- Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Harry Marshall
- Schulich School of Medicine & Dentistry, Western University, Ontario, Canada
| | - Qianye Yang
- Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Tom Syer
- Centre for Medical Imaging, University College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, London, UK
| | - Mark Emberton
- Division of Surgery & Interventional Science, University College London, London, UK
| | - Dean C Barratt
- Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Carmen C M Cho
- Prince of Wales Hospital and Department of Imaging and Intervention Radiology, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yipeng Hu
- Centre for Medical Image Computing, Wellcome/EPSRC Centre for Interventional & Surgical Sciences, Department of Medical Physics & Biomedical Engineering, University College London, London, UK
| | - Bernard Chiu
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
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Kim H, Kang SW, Kim JH, Nagar H, Sabuncu M, Margolis DJA, Kim CK. The role of AI in prostate MRI quality and interpretation: Opportunities and challenges. Eur J Radiol 2023; 165:110887. [PMID: 37245342 DOI: 10.1016/j.ejrad.2023.110887] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 05/06/2023] [Accepted: 05/20/2023] [Indexed: 05/30/2023]
Abstract
Prostate MRI plays an important role in imaging the prostate gland and surrounding tissues, particularly in the diagnosis and management of prostate cancer. With the widespread adoption of multiparametric magnetic resonance imaging in recent years, the concerns surrounding the variability of imaging quality have garnered increased attention. Several factors contribute to the inconsistency of image quality, such as acquisition parameters, scanner differences and interobserver variabilities. While efforts have been made to standardize image acquisition and interpretation via the development of systems, such as PI-RADS and PI-QUAL, the scoring systems still depend on the subjective experience and acumen of humans. Artificial intelligence (AI) has been increasingly used in many applications, including medical imaging, due to its ability to automate tasks and lower human error rates. These advantages have the potential to standardize the tasks of image interpretation and quality control of prostate MRI. Despite its potential, thorough validation is required before the implementation of AI in clinical practice. In this article, we explore the opportunities and challenges of AI, with a focus on the interpretation and quality of prostate MRI.
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Affiliation(s)
- Heejong Kim
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Shin Won Kang
- Research Institute for Future Medicine, Samsung Medical Center, Republic of Korea
| | - Jae-Hun Kim
- Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
| | - Himanshu Nagar
- Department of Radiation Oncology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021, United States
| | - Mert Sabuncu
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States
| | - Daniel J A Margolis
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St Box 141, New York, NY 10021, United States.
| | - Chan Kyo Kim
- Department of Radiology and Center for Imaging Science, Samsung Medical Center, Sungkyunkwan University School of Medicine, Republic of Korea
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Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5497120. [PMID: 35669675 PMCID: PMC9167116 DOI: 10.1155/2022/5497120] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 03/22/2022] [Accepted: 04/21/2022] [Indexed: 11/17/2022]
Abstract
The SEMRCNN model is proposed for autonomously extracting prostate cancer locations from regions of multiparametric magnetic resonance imaging (MP-MRI). Feature maps are explored in order to provide fine segmentation based on the candidate regions. Two parallel convolutional networks retrieve these maps of apparent diffusion coefficient (ADC) and T2W images, which are then integrated to use the complimentary information in MP-MRI. By utilizing extrusion and excitation blocks, it is feasible to automatically increase the number of relevant features in the fusion feature map. The aim of this study is to study the current scenario of the SE Mask-RCNN and deep convolutional network segmentation model that can automatically identify prostate cancer in the MP-MRI prostatic region. Experiments are conducted using 140 instances. SEMRCNN segmentation of prostate cancer lesions has a Dice coefficient of 0.654, a sensitivity of 0.695, a specificity of 0.970, and a positive predictive value of 0.685. SEMRCNN outperforms other models like as V net, Resnet50-U-net, Mask-RCNN, and U network model for prostate cancer MP-MRI segmentation. This approach accomplishes fine segmentation of lesions by recognizing and finding potential locations of prostate cancer lesions, eliminating interference from surrounding areas, and improving the learning of the lesions’ features.
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Saha A, Hosseinzadeh M, Huisman H. End-to-end prostate cancer detection in bpMRI via 3D CNNs: Effects of attention mechanisms, clinical priori and decoupled false positive reduction. Med Image Anal 2021; 73:102155. [PMID: 34245943 DOI: 10.1016/j.media.2021.102155] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2021] [Revised: 05/30/2021] [Accepted: 06/08/2021] [Indexed: 01/22/2023]
Abstract
We present a multi-stage 3D computer-aided detection and diagnosis (CAD) model2 for automated localization of clinically significant prostate cancer (csPCa) in bi-parametric MR imaging (bpMRI). Deep attention mechanisms drive its detection network, targeting salient structures and highly discriminative feature dimensions across multiple resolutions. Its goal is to accurately identify csPCa lesions from indolent cancer and the wide range of benign pathology that can afflict the prostate gland. Simultaneously, a decoupled residual classifier is used to achieve consistent false positive reduction, without sacrificing high sensitivity or computational efficiency. In order to guide model generalization with domain-specific clinical knowledge, a probabilistic anatomical prior is used to encode the spatial prevalence and zonal distinction of csPCa. Using a large dataset of 1950 prostate bpMRI paired with radiologically-estimated annotations, we hypothesize that such CNN-based models can be trained to detect biopsy-confirmed malignancies in an independent cohort. For 486 institutional testing scans, the 3D CAD system achieves 83.69±5.22% and 93.19±2.96% detection sensitivity at 0.50 and 1.46 false positive(s) per patient, respectively, with 0.882±0.030 AUROC in patient-based diagnosis -significantly outperforming four state-of-the-art baseline architectures (U-SEResNet, UNet++, nnU-Net, Attention U-Net) from recent literature. For 296 external biopsy-confirmed testing scans, the ensembled CAD system shares moderate agreement with a consensus of expert radiologists (76.69%; kappa = 0.51±0.04) and independent pathologists (81.08%; kappa = 0.56±0.06); demonstrating strong generalization to histologically-confirmed csPCa diagnosis.
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Affiliation(s)
- Anindo Saha
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen 6525 GA, the Netherlands.
| | - Matin Hosseinzadeh
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen 6525 GA, the Netherlands
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, Nijmegen 6525 GA, the Netherlands
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Brain tissues have single-voxel signatures in multi-spectral MRI. Neuroimage 2021; 234:117986. [PMID: 33757906 DOI: 10.1016/j.neuroimage.2021.117986] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Revised: 03/03/2021] [Accepted: 03/15/2021] [Indexed: 12/20/2022] Open
Abstract
Since the seminal works by Brodmann and contemporaries, it is well-known that different brain regions exhibit unique cytoarchitectonic and myeloarchitectonic features. Transferring the approach of classifying brain tissues - and other tissues - based on their intrinsic features to the realm of magnetic resonance (MR) is a longstanding endeavor. In the 1990s, atlas-based segmentation replaced earlier multi-spectral classification approaches because of the large overlap between the class distributions. Here, we explored the feasibility of performing global brain classification based on intrinsic MR features, and used several technological advances: ultra-high field MRI, q-space trajectory diffusion imaging revealing voxel-intrinsic diffusion properties, chemical exchange saturation transfer and semi-solid magnetization transfer imaging as a marker of myelination and neurochemistry, and current neural network architectures to analyze the data. In particular, we used the raw image data as well to increase the number of input features. We found that a global brain classification of roughly 97 brain regions was feasible with gross classification accuracy of 60%; and that mapping from voxel-intrinsic MR data to the brain region to which the data belongs is possible. This indicates the presence of unique MR signals of different brain regions, similar to their cytoarchitectonic and myeloarchitectonic fingerprints.
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Lin M, Chen W, Zhao M, Gibson E, Bastian-Jordan M, Cool DW, Kassam Z, Liang H, Chow TW, Ward AD, Chiu B. Prostate lesion delineation from multiparametric magnetic resonance imaging based on locality alignment discriminant analysis. Med Phys 2018; 45:4607-4618. [DOI: 10.1002/mp.13155] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2018] [Revised: 06/07/2018] [Accepted: 08/17/2018] [Indexed: 11/05/2022] Open
Affiliation(s)
- Mingquan Lin
- Department of Electronic Engineering; City University of Hong Kong; Hong Kong China
| | - Weifu Chen
- School of Mathematics; Sun Yat-sen University; Guangzhou Guangdong China
- Department of Electronic Engineering; City University of Hong Kong; Hong Kong China
| | - Mingbo Zhao
- School of Information Science and Technology; Donghua University; Shanghai China
| | - Eli Gibson
- Biomedical Engineering; University of Western Ontario; London Ontario Canada
- Centre for Medical Image Computing; University College London; London UK
| | | | - Derek W. Cool
- Department of Medical Imaging; University of Western Ontario; London Ontario Canada
| | - Zahra Kassam
- Department of Medical Imaging; University of Western Ontario; London Ontario Canada
- Lawson Health Research Institute; London Ontario Canada
| | - Huageng Liang
- Department of Urology; Union Hospital; Tongji Medical College; Huazhong University of Science and Technology; Wuhan Hubei China
| | - Tommy W.S. Chow
- Department of Electronic Engineering; City University of Hong Kong; Hong Kong China
| | - Aaron D. Ward
- Department of Medical Biophysics; University of Western Ontario; London Ontario Canada
- Lawson Health Research Institute; London Ontario Canada
| | - Bernard Chiu
- Department of Electronic Engineering; City University of Hong Kong; Hong Kong China
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Chen W, Lin M, Gibson E, Bastian-Jordan M, Cool DW, Kassam Z, Liang H, Feng G, Ward AD, Chiu B. A self-tuned graph-based framework for localization and grading prostate cancer lesions: An initial evaluation based on multiparametric magnetic resonance imaging. Comput Biol Med 2018; 96:252-265. [DOI: 10.1016/j.compbiomed.2018.03.017] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 03/29/2018] [Accepted: 03/29/2018] [Indexed: 11/26/2022]
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Rampun A, Tiddeman B, Zwiggelaar R, Malcolm P. Computer aided diagnosis of prostate cancer: A texton based approach. Med Phys 2016; 43:5412. [PMID: 27782724 PMCID: PMC5035312 DOI: 10.1118/1.4962031] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2016] [Revised: 08/02/2016] [Accepted: 08/19/2016] [Indexed: 12/19/2022] Open
Abstract
PURPOSE In this paper the authors propose a texton based prostate computer aided diagnosis approach which bypasses the typical feature extraction process such as filtering and convolution which can be computationally expensive. The study focuses the peripheral zone because 75% of prostate cancers start within this region and the majority of prostate cancers arising within this region are more aggressive than those arising in the transitional zone. METHODS For the model development, square patches were extracted at random locations from malignant and benign regions. Subsequently, extracted patches were aggregated and clustered using k-means clustering to generate textons that represent both regions. All textons together form a texton dictionary, which was used to construct a texton map for every peripheral zone in the training images. Based on the texton map, histogram models for each malignant and benign tissue samples were constructed and used as a feature vector to train our classifiers. In the testing phase, four machine learning algorithms were employed to classify each unknown sample tissue based on its corresponding feature vector. RESULTS The proposed method was tested on 418 T2-W MR images taken from 45 patients. Evaluation results show that the best three classifiers were Bayesian network (Az = 92.8% ± 5.9%), random forest (89.5% ± 7.1%), and k-NN (86.9% ± 7.5%). These results are comparable to the state-of-the-art in the literature. CONCLUSIONS The authors have developed a prostate computer aided diagnosis method based on textons using a single modality of T2-W MRI without the need for the typical feature extraction methods, such as filtering and convolution. The proposed method could form a solid basis for a multimodality magnetic resonance imaging based systems.
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Affiliation(s)
- Andrik Rampun
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3DB, United Kingdom
| | - Bernie Tiddeman
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3DB, United Kingdom
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth, Ceredigion SY23 3DB, United Kingdom
| | - Paul Malcolm
- Department of Radiology, Norfolk & Norwich University Hospital, Norwich NR4 7UY, United Kingdom
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Liu L, Tian Z, Zhang Z, Fei B. Computer-aided Detection of Prostate Cancer with MRI: Technology and Applications. Acad Radiol 2016; 23:1024-46. [PMID: 27133005 PMCID: PMC5355004 DOI: 10.1016/j.acra.2016.03.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2015] [Revised: 03/18/2016] [Accepted: 03/21/2016] [Indexed: 01/10/2023]
Abstract
One in six men will develop prostate cancer in his lifetime. Early detection and accurate diagnosis of the disease can improve cancer survival and reduce treatment costs. Recently, imaging of prostate cancer has greatly advanced since the introduction of multiparametric magnetic resonance imaging (mp-MRI). Mp-MRI consists of T2-weighted sequences combined with functional sequences including dynamic contrast-enhanced MRI, diffusion-weighted MRI, and magnetic resonance spectroscopy imaging. Because of the big data and variations in imaging sequences, detection can be affected by multiple factors such as observer variability and visibility and complexity of the lesions. To improve quantitative assessment of the disease, various computer-aided detection systems have been designed to help radiologists in their clinical practice. This review paper presents an overview of literatures on computer-aided detection of prostate cancer with mp-MRI, which include the technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application.
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Affiliation(s)
- Lizhi Liu
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Zhiqiang Tian
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329
| | - Zhenfeng Zhang
- Center of Medical Imaging and Image-guided Therapy, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology Collaborative Innovation Center for Cancer Medicine, 651 Dongfeng Road East, Guangzhou, 510060, China
| | - Baowei Fei
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, 1841 Clifton Road NE, Atlanta, GA 30329; Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, 1841 Clifton Road NE, Atlanta, Georgia 30329; Winship Cancer Institute of Emory University, 1841 Clifton Road NE, Atlanta, Georgia 30329.
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Rampun A, Zheng L, Malcolm P, Tiddeman B, Zwiggelaar R. Computer-aided detection of prostate cancer in T2-weighted MRI within the peripheral zone. Phys Med Biol 2016; 61:4796-825. [DOI: 10.1088/0031-9155/61/13/4796] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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Rampun A, Chen Z, Malcolm P, Tiddeman B, Zwiggelaar R. Computer-aided diagnosis: detection and localization of prostate cancer within the peripheral zone. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2016; 32:e02745. [PMID: 26313267 DOI: 10.1002/cnm.2745] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2014] [Revised: 05/21/2015] [Accepted: 08/24/2015] [Indexed: 06/04/2023]
Abstract
We propose a methodology for prostate cancer detection and localization within the peripheral zone based on combining multiple segmentation techniques. We extract four image features using Gaussian and median filters. Subsequently, we use each image feature separately to generate binary segmentations. Finally, we take the intersection of all four binary segmentations, incorporating a model of the peripheral zone, and perform erosion to remove small false-positive regions. The initial evaluation of this method is based on 275 MRI images from 37 patients, and 86% of the slices were classified correctly with 87% and 86% sensitivity and specificity achieved, respectively. This paper makes two contributions: firstly, a novel computer-aided diagnosis approach, which is based on combining multiple segmentation techniques using only a small number of simple image features, and secondly, the development of the proposed method and its application in prostate cancer detection and localization using a single MRI modality with the results comparable with the state-of-the-art multimodality and advanced computer vision methods in the literature. Copyright © 2015 John Wiley & Sons, Ltd.
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Affiliation(s)
- Andrik Rampun
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
| | - Zhili Chen
- Information and Control Engineering Faculty, Shenyang Jianzhu University, Liaoning, 110168, China
| | - Paul Malcolm
- Department of Radiology, Norfolk Norwich University Hospital, Norwich, NR4 7UY, UK
| | - Bernie Tiddeman
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
| | - Reyer Zwiggelaar
- Department of Computer Science, Aberystwyth University, Aberystwyth, SY23 3DB, UK
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Kwak JT, Xu S, Wood BJ, Turkbey B, Choyke PL, Pinto PA, Wang S, Summers RM. Automated prostate cancer detection using T2-weighted and high-b-value diffusion-weighted magnetic resonance imaging. Med Phys 2016; 42:2368-78. [PMID: 25979032 DOI: 10.1118/1.4918318] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
PURPOSE The authors propose a computer-aided diagnosis (CAD) system for prostate cancer to aid in improving the accuracy, reproducibility, and standardization of multiparametric magnetic resonance imaging (MRI). METHODS The proposed system utilizes two MRI sequences [T2-weighted MRI and high-b-value (b = 2000 s/mm(2)) diffusion-weighted imaging (DWI)] and texture features based on local binary patterns. A three-stage feature selection method is employed to provide the most discriminative features. The authors included a total of 244 patients. Training the CAD system on 108 patients (78 MR-positive prostate cancers and 105 benign MR-positive lesions), two validation studies were retrospectively performed on 136 patients (68 MR-positive prostate cancers, 111 benign MR-positive lesions, and 117 MR-negative benign lesions). RESULTS In distinguishing cancer from MR-positive benign lesions, an area under receiver operating characteristic curve (AUC) of 0.83 [95% confidence interval (CI): 0.76-0.89] was achieved. For cancer vs MR-positive or MR-negative benign lesions, the authors obtained an AUC of 0.89 AUC (95% CI: 0.84-0.93). The performance of the CAD system was not dependent on the specific regions of the prostate, e.g., a peripheral zone or transition zone. Moreover, the CAD system outperformed other combinations of MRI sequences: T2W MRI, high-b-value DWI, and the standard apparent diffusion coefficient (ADC) map of DWI. CONCLUSIONS The novel CAD system is able to detect the discriminative texture features for cancer detection and localization and is a promising tool for improving the quality and efficiency of prostate cancer diagnosis.
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Affiliation(s)
- Jin Tae Kwak
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892
| | - Sheng Xu
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892
| | - Bradford J Wood
- Center for Interventional Oncology, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892
| | - Baris Turkbey
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892
| | - Peter L Choyke
- Molecular Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892
| | - Peter A Pinto
- Urologic Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, Maryland 20892
| | - Shijun Wang
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892
| | - Ronald M Summers
- Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Clinical Center, National Institutes of Health, Bethesda, Maryland 20892
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Derraz F, Forzy G, Delebarre A, Taleb-Ahmed A, Oussalah M, Peyrodie L, Verclytte S. Prostate contours delineation using interactive directional active contours model and parametric shape prior model. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2015; 31. [PMID: 26009857 DOI: 10.1002/cnm.2726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2013] [Revised: 05/17/2015] [Accepted: 05/17/2015] [Indexed: 06/04/2023]
Abstract
Prostate contours delineation on Magnetic Resonance (MR) images is a challenging and important task in medical imaging with applications of guiding biopsy, surgery and therapy. While a fully automated method is highly desired for this application, it can be a very difficult task due to the structure and surrounding tissues of the prostate gland. Traditional active contours-based delineation algorithms are typically quite successful for piecewise constant images. Nevertheless, when MR images have diffuse edges or multiple similar objects (e.g. bladder close to prostate) within close proximity, such approaches have proven to be unsuccessful. In order to mitigate these problems, we proposed a new framework for bi-stage contours delineation algorithm based on directional active contours (DAC) incorporating prior knowledge of the prostate shape. We first explicitly addressed the prostate contour delineation problem based on fast globally DAC that incorporates both statistical and parametric shape prior model. In doing so, we were able to exploit the global aspects of contour delineation problem by incorporating a user feedback in contours delineation process where it is shown that only a small amount of user input can sometimes resolve ambiguous scenarios raised by DAC. In addition, once the prostate contours have been delineated, a cost functional is designed to incorporate both user feedback interaction and the parametric shape prior model. Using data from publicly available prostate MR datasets, which includes several challenging clinical datasets, we highlighted the effectiveness and the capability of the proposed algorithm. Besides, the algorithm has been compared with several state-of-the-art methods.
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Affiliation(s)
- Foued Derraz
- Telecommunications Laboratory, Technology Faculty, Abou Bekr Belkaïd University, Tlemcen, 13000, Algeria
- Université Nord de France, F-59000, Lille, France
- Unité de Traitement de Signaux Biomédicaux, Faculté de médecine et maïeutique, Lille, France
- LAMIH UMR CNRS 8201, Le Mont Houy, Université de Valenciennes et Cambresis, 59313, Valenciennes, France
| | - Gérard Forzy
- Unité de Traitement de Signaux Biomédicaux, Faculté de médecine et maïeutique, Lille, France
- Groupement des Hopitaux de l'́Institut Catholique de Lille, France
| | - Arnaud Delebarre
- Groupement des Hopitaux de l'́Institut Catholique de Lille, France
| | - Abdelmalik Taleb-Ahmed
- Université Nord de France, F-59000, Lille, France
- LAMIH UMR CNRS 8201, Le Mont Houy, Université de Valenciennes et Cambresis, 59313, Valenciennes, France
| | - Mourad Oussalah
- School of Electronics, Electrical and Computer Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Laurent Peyrodie
- Université Nord de France, F-59000, Lille, France
- Hautes Etudes dÍngénieur, 13 rue de Toul, 59000, Lille, France
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Artan Y, Oto A, Yetik IS. Cross-device automated prostate cancer localization with multiparametric MRI. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:5385-5394. [PMID: 24236301 DOI: 10.1109/tip.2013.2285626] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Prostate cancer localization using supervised classification techniques has aroused considerable interest in medical imaging community in recent years. However, it is crucial to have an accurate training data set for supervised classification techniques. Since different devices with, e.g., different protocols and/or field strengths cause different intensity profiles, each device/protocol must have an accompanying training data set, which is very costly to obtain. It is highly desirable to adapt the existing classifier(s) trained for one device/protocol to help classify data coming from another device/protocol. In this paper, we propose a novel method that has the ability to design classifiers obtained from one imaging protocol and/or MRI device to be used on a data set from another protocol and/or imaging device. As an example problem, we consider prostate cancer localization with multiparametric MRI. We show that simple normalization techniques such as z-score are not sufficient for cross-device automated cancer localization. On the other hand, the method we have originally developed based on relative intensity allows us to successfully use a classifier obtained from one device to be applied on a test patient imaged with another device. Proposed method also allows us to employ T2-weighted MR images directly instead of an additional step to normalize T2-weighted images usually performed in an ad hoc manner when T2 maps are not available. To demonstrate the effectiveness of the proposed method, we use a multiparametric MRI data set acquired from 18 biopsy-confirmed cancer patients with two separate scanners: 1) 1.5-T (Excite HD) GE and 2) 1.5-T (Achieva) Philips Healthcare scanners. A comprehensive visual, quantitative, and statistical analysis of the results show that methods we have developed allow us to: 1) perform cross-device automated classification and 2) use T2-weighted images without an ad hoc subject-specific normalization.
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