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computer-aided detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI)

JOURNAL OF MAGNETIC RESONANCE IMAGING 2025年第6期 2025 Jan 9页

作者： Jannatdoust, Payam Valizadeh, Parya Saeedi, Nikoo Valizadeh, Gelareh Salari, Hanieh Mobarak Rad, Hamidreza Saligheh Gity, Masoumeh Univ Tehran Med Sci Sch Med Tehran Iran Islamic Azad Univ Student Res Comm Mashhad Branch Mashhad Iran Univ Tehran Med Sci Quantitat MR Imaging & Spect Grp QMISG Tehran Iran Univ Tehran Med Sci Dept Med Phys & Biomed Engn Tehran Iran Univ Tehran Med Sci Adv Diagnost & Intervent Radiol Res Ctr Tehran Iran

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical *** of EvidenceNATechnical EfficacyStage 2

关键词： breast cancer artificial intelligence lesion segmentation magnetic resonance imaging (MRI) computer-aided detection

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computer-aided detection systems based on ballistocardiography signals: A review

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 151卷

作者： Cimr, Dalibor Busovsky, Damian Fujita, Hamido Studnicka, Filip Cimler, Richard Univ Hradec Kralove Fac Sci Rokitanskeho 62 Hradec Kralove 50003 Czech Republic Univ Teknol Malaysia Malaysia Japan Int Inst Technol MJIIT Kuala Lumpur Malaysia Princess Nourah Bint Abdulrahman Univ PNU Coll Sci Riyadh 13412 Saudi Arabia Iwate Prefectural Univ Reg Res Ctr Takizawa Iwate 0200693 Japan

This comprehensive review paper seeks to provide an in-depth survey of the technologies and methodologies employed in decision support systems for ballistocardiography. The paper extensively covers the biometric information embedded in the measured ballistocardiography signals. The presented exploration of ballistocardiography covers various pivotal stages, including signal measurement techniques, pre-processing methodologies, feature extraction approaches, classification techniques, and evaluation methods. Within the scope of this study, a systematic review has been performed, bringing together notable strategies employed in ballistocardiography from its initial stages to its current state of advancement. The efficacy of these systems in estimating ballistocardiography-based biometrics has demonstrated noteworthy proximity to acceptable levels. The utilization of ballistocardiography signals holds significant promise as an evolving field of research. This paper concludes by addressing the limitations inherent in the current state of research, outlining the potential directions for future investigations and real-world applications, and discussing the crucial aspect of explainability, which represents one of the new trends in computer-aided detection requirements.

关键词： computer-aided detection Ballistocardiography Decision support system

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AGA Living Clinical Practice Guideline on computer-aided detection-Assisted Colonoscopy

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GASTROENTEROLOGY 2025年第4期168卷 691-700页

作者： Sultan, Shahnaz Shung, Dennis L. Kolb, Jennifer M. Foroutan, Farid Hassan, Cesare Kahi, Charles J. Liang, Peter S. Levin, Theodore R. Siddique, Shazia Mehmood Lebwohl, Benjamin Univ Minnesota Div Gastroenterol Hepatol & Nutr Minneapolis MN USA Minneapolis Vet Affairs Healthcare Syst Minneapolis MN USA Yale Sch Med Dept Med Sect Digest Dis New Haven CT USA Univ Calif Los Angeles David Geffen Sch Med Vatche & Tamar Manoukian Div Digest Dis Los Angeles CA USA Vet Affairs Greater Angeles Healthcare Syst Div Gastroenterol Hepatol & Parenteral Nutr Los Angeles CA USA MAGIC Evidence Ecosyst Fdn Oslo Norway Univ Hlth Network Ted Rogers Ctr Heart Res Toronto ON Canada IRCCS Humanitas Res Hosp Milan Italy Humanitas Univ Dept Biomed Sci Milan Italy Indiana Univ Dept Gastroenterol Med Ctr Indianapolis IN USA NYU Langone Hlth Dept Med Div Gastroenterol & Hepatol New York NY USA Vet Affairs New York Harbor Hlth Care Syst Dept Med New York NY USA Kaiser Permanente Northern Calif Div Res Pleasanton CA USA Kaiser Permanente Walnut Creek Dept Gastroenterol Walnut Creek CA USA Univ Penn Div Gastroenterol Philadelphia PA USA Univ Penn Leonard Davis Inst Hlth Econ Philadelphia PA USA Univ Penn Ctr Healthcare Improvement & Patient Safety Philadelphia PA USA Columbia Univ Irving Med Ctr Dept Med New York NY USA Columbia Univ Mailman Sch Publ Hlth Dept Epidemiol New York NY USA

BACKGROUND & AIMS: This American Gastroenterological Association (AGA) guideline is intended to provide an overview of the evidence and support endoscopists and patients on the use of computer-aided detection (CADe) systems for the detection of colorectal polyps during colonoscopy. METHODS: A multidisciplinary panel of content experts and guideline methodologists used the Grading of Recommendations Assessment, Development and Evaluation framework and relied on the following sources of evidence: (1) a systematic review examining the desirable and undesirable effects (ie, benefits and harms) of CADe-assisted colonoscopy, (2) a microsimulation study estimating the effects of CADe on longer-term patient-important outcomes, (3) a systematic search of evidence evaluating the values and preferences of patients undergoing colonoscopy, and (4) a systematic review of studies evaluating health care providers' trust in artificial intelligence technology in gastroenterology. RESULTS: The panel reached the conclusion that no recommendation could be made for or against the use of CADe-assisted colonoscopy in light of very low certainty of evidence for the critical outcomes, desirable and undesirable (11 fewer colorectal cancers per 10,000 individuals and 2 fewer colorectal cancer deaths per 10,000 individuals), increased burden of more intensive surveillance colonoscopies (635 more per 10,000 individuals), and cost and resource implications. The panel acknowledged the 8% (95% CI, 6%-10%) increase in adenoma detection rate and 2% (95% CI, 0%-4%) increase in advanced adenoma and/or sessile serrated lesion detection rate. CONCLUSIONS: This guideline highlights the close tradeoff between desirable and undesirable effects and the limitations in the current evidence to support a recommendation. The panel acknowledged the potential for CADe to continually improve as an iterative artificial intelligence application. Ongoing publications providing evidence for critical outcomes will help i

关键词： Artificial Intelligence computer-aided detection Colonoscopy Colorectal Cancer

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Performance of computer-aided detection Software in Tuberculosis Case Finding in Township Health Centers in China

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Chronic Diseases and Translational Medicine 2025年

作者： Cao, Xuefang Feng, Boxuan Zhang, Bin Wang, Dakuan Du, Jiang He, Yijun Guo, Tonglei Pan, Shouguo Liu, Zisen Yan, Jiaoxia Jin, Qi Gao, Lei Xin, Henan NHC Key Laboratory of Systems Biology of Pathogens National Institute of Pathogen Biology and Center for Tuberculosis Research Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Pathogen Infection Prevention and Control (Ministry of Education) National Institute of Pathogen Biology Chinese Academy of Medical Sciences & Peking Union Medical College Beijing China Center for Disease Control and Prevention of Zhongmu County Henan Zhengzhou China

Background: computer-aided detection (CAD) software has been introduced to automatically interpret digital chest X-rays. This study aimed to evaluate the performance of CAD software (JF CXR-1 v3.0, which was developed by a domestic Hi-tech enterprise) in tuberculosis (TB) case finding in China. Methods: In 2019, we conducted an internal evaluation of the performance of JF CXR-1 v3.0 by reading standard images annotated by a panel of experts. In 2020, using the reading results of chest X-rays by a panel of experts as the reference standard, we conducted an on-site prospective study to evaluate the performance of JF CXR-1 v3.0 and local radiologists in TB case finding in 13 township health centers in Zhongmu County, Henan Province. Results: Internal assessment results based on 277 standard images showed that JF CXR-1 v3.0 had a sensitivity of 85.94% (95% confidence interval [CI]: 77.42%, 94.45%) and a specificity of 74.65% (95% CI: 68.81%, 80.49%) to distinguish active TB from other imaging conditions. In the on-site evaluation phase, images from 3705 outpatients who underwent chest X-ray detection were read by JF CXR-1 v3.0 and local radiologists in parallel. The imaging diagnosis of local radiologists for active TB had a sensitivity of 32.89% (95% CI: 22.33%, 43.46%) and a specificity of 99.28% (95% CI: 99.01%, 99.56%), while JF CXR-1 v3.0 showed a significantly higher sensitivity of 92.11% (95% CI: 86.04%, 98.17%) (p < 0.05) and maintained high specificity at 94.54% (95% CI: 93.81%, 95.28%). Conclusions: CAD software could play a positive role in improving the TB case finding capability of township health centers. © 2025 The Author(s). Chronic Diseases and Translational Medicine published by John Wiley & Sons, Ltd on behalf of Chinese Medical Association.

关键词： artificial intelligence case finding chest X-ray computer-aided detection tuberculosis

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The performance of computer-aided detection for chest radiography in tuberculosis screening: a population-based retrospective cohort study

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EMERGING MICROBES & INFECTIONS 2025年第1期14卷 2470998页

作者： Xin, Henan Wang, Wei Wang, Xiaomeng Huang, Juanjuan Di, Yuanzhi Du, Jiang Cao, Xuefang Feng, Boxuan Shen, Lingyu He, Yijun Guo, Tonglei Li, Zihan Liang, Jianguo Wang, Zhen Zhu, Ping Gao, Lei Chinese Acad Med Sci & Peking Union Med Coll Natl Inst Pathogen Biol NHC Key Lab Syst Biol Pathogens 16 Tianrong St Beijing 102629 Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Ctr TB Res Beijing Peoples R China Chinese Acad Med Sci & Peking Union Med Coll Natl Inst Pathogen Biol Key Lab Pathogen Infect Prevent & Control Minist Educ Beijing Peoples R China Quzhou City Ctr Dis Control & Prevent 154 Xian Rd Quzhou 324003 Peoples R China Zhejiang Prov Ctr Dis Control & Prevent 3399 Binsheng Rd Hangzhou 310009 Peoples R China

From 2020 to 2022, a pulmonary tuberculosis (PTB) active case finding project based on chest X-ray (CXR) examination was conducted targeting individuals aged >= 65 years old in Jiangshan County, Quzhou City. The current study used computer-aided detection (CAD) software (JF CXR-1 v2) to retrospectively analyze the CXR images and to estimate its potential capacity for identifying PTB cases. The information of notified microbiologically confirmed PTB among the participants were exported from the Tuberculosis Information Management System. A total of 49,919 subjects participated in the 2020 examinations. Of these, 40,741 and 39,185 completed the follow-up surveys in 2021 and 2022, respectively. The pooled prevalence of suspected PTB reported by radiologists was 1.21% (1579/129,776), compared with 12.43% (16,129/129,776) reported by CAD. Of 101 bacteriologically confirmed PTB cases notified over three years, radiologists and CAD reported 45.54% (46/101) and 83.16% (84/101) as suspected cases, respectively. Among subjects with abnormal CAD (CAD score>0.35), the majority of the notified confirmed PTB patients (63/84) had their CAD scores >75% quantiles (as>0.75). With 3 years' results, their CAD scores exhibited dynamic changes along with disease progression or treatment with median scores peaking in the year of diagnosis. This intriguing finding suggests that CAD for CXR reading assisted radiologists in PTB screening by reducing workload and improving case finding. The CAD primary score may have the potential to identify high-risk individuals and early PTB patients, adding a new dimension to our understanding of disease progression.

关键词： Pulmonary tuberculosis screening computer-aided detection chest radiography early finding

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Creating a standardized tool for the evaluation and comparison of artificial intelligence–based computer-aided detection programs in colonoscopy: a modified Delphi approach

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Gastrointestinal Endoscopy 2025年

作者： Gadi, Sanjay R.V. Mori, Yuichi Misawa, Masashi East, James E. Hassan, Cesare Repici, Alessandro Byrne, Michael F. von Renteln, Daniel Hewett, David G. Wang, Pu Saito, Yutaka Matsubayashi, Carolina Ogawa Ahmad, Omer F. Sharma, Prateek Gross, Seth A. Sengupta, Neil Mansour, Nabil Cherubini, Andrea Dinh, Nhan Ngo Xiao, Xiao Mountney, Peter González-Bueno Puyal, Juana Little, Greg LaRocco, Shawn Conjeti, Sailesh Seibt, Hannes Zur, Dror Shimada, Hitoshi Berzin, Tyler M. Glissen Brown, Jeremy R. Department of Medicine Duke University School of Medicine Durham NC United States Digestive Disease Center Showa University Northern Yokohama Hospital Yokohama Japan Clinical Effectiveness Research Group Institute of Health and Society University of Oslo Oslo Norway Department of Transplantation Medicine Oslo University Hospital Oslo Norway Translational Gastroenterology Unit John Radcliffe Hospital Oxford United Kingdom Oxford NIHR Biomedical Research Centre University of Oxford Oxford United Kingdom Department of Biomedical Sciences Humanitas University Pieve Emanuele Milan Italy IRCCS Humanitas Research Hospital Rozzano Milan Italy Division of Gastroenterology Vancouver General Hospital University of British Columbia Vancouver BC Canada Satisfai Health Vancouver BC Canada Division of Gastroenterology Montréal University Hospital and Research Center Montréal Québec Canada School of Medicine The University of Queensland Brisbane QLD Australia Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital Chengdu China Endoscopy Division National Cancer Center Hospital Tokyo Japan Gastrointestinal Endoscopy Unit Gastroenterology Department University of São Paulo Medical School São Paulo Brazil AI Medical Services Inc. Tokyo Japan Wellcome/EPSRC Centre for Interventional & Surgical Sciences University College London London United Kingdom Division of Gastroenterology and Hepatology University of Kansas School of Medicine and VA Medical Center Kansas City KS United States Division of Gastroenterology and Hepatology New York University Langone Health System New York NY United States Section of Gastroenterology University of Chicago Medicine Chicago IL United States Section of Gastroenterology and Hepatology Baylor College of Medicine Houston TX United States Cosmo Intelligent Medical Devices Dublin Ireland Wision AI Palo Alto CA United States Odin Vision London United Kingdom Olympus Corporation Tokyo Japan Pentax Medical

Background and Aims: Multiple computer-aided detection (CADe) software programs have now achieved regulatory approval in the United States, Europe, and Asia and are being used in routine clinical practice to support colorectal cancer screening. There is uncertainty regarding how different CADe algorithms may perform. No objective methodology exists for comparing different algorithms. We aimed to identify priority scoring metrics for CADe evaluation and comparison. Methods: A modified Delphi approach was used. Twenty-five global leaders in CADe in colonoscopy, including endoscopists, researchers, and industry representatives, participated in an online survey over the course of 8 months. Participants generated 121 scoring criteria, 54 of which were deemed within the study scope and distributed for review and asynchronous e-mail–based open comment. Participants then scored criteria in order of priority on a 5-point Likert scale during ranking round 1. The top 11 highest priority criteria were re-distributed, with another opportunity for open comment, followed by a final round of priority scoring to identify the final 6 criteria. Results: Mean priority scores for the 54 criteria ranged from 2.25 to 4.38 after the first ranking round. The top 11 criteria after round 1 of ranking yielded mean priority scores ranging from 3.04 to 4.16. The final 6 highest priority criteria, including a tie for first-place ranking, were (1, tied) sensitivity (average, 4.16) and (1, tied) separate and independent validation of the CADe algorithm (average, 4.16);(3) adenoma detection rate (average, 4.08);(4) false-positive rate (average, 4.00);(5) latency (average, 3.84);and (6) adenoma miss rate (average, 3.68). Conclusions: This is the first reported international consensus statement of priority scoring metrics for CADe in colonoscopy. These scoring criteria should inform CADe software development and refinement. Future research should validate these metrics on a benchmark video data set to

关键词： Colonoscopy adenoma detection rate artificial intelligence computer-aided detection sensitivity

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computer-aided detection of interstitial abnormalities in chest radiographs using a reference standard based on computed tomography

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MEDICAL PHYSICS 2007年第12期34卷 4798-4809页

作者： Arzhaevaa, Yulia Prokop, Mathias Tax, David M. J. De Jong, Pim A. Schaefer-Prokop, Cornelia M. van Ginneken, Bram Univ Med Ctr Utrecht Images Sci Inst Utrecht Netherlands Univ Med Ctr Utrecht Dept Radiol Utrecht Netherlands Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Netherlands Meander Med Ctr Dept Radiol Amersfoort Netherlands Univ Amsterdam Acad Med Ctr Dept Radiol NL-1105 AZ Amsterdam Netherlands

A computer-aided detection (CAD) system is presented for the localization of interstitial lesions in chest radiographs. The system analyzes the complete lung fields using a two-class supervised pattern classification approach to distinguish between normal texture and texture affected by interstitial lung disease. Analysis is done pixel-wise and produces a probability map for an image where each pixel in the lung fields is assigned a probability of being abnormal. Interstitial lesions are often subtle and ill defined on x-rays and hence difficult to detect, even for expert radiologists. Therefore a new, semiautomatic method is proposed for setting a reference standard for training and evaluating the CAD system. The proposed method employs the fact that interstitial lesions are more distinct on a computed tomography (CT) scan than on a radiograph. Lesion outlines, manually drawn on coronal slices of a CT scan of the same patient, are automatically transformed to corresponding outlines on the chest x-ray, using manually indicated correspondences for a small set of anatomical landmarks. For the texture analysis, local structures are described by means of the multiscale Gaussian filter bank. The system performance is evaluated with ROC analysis on a database of digital chest radiographs containing 44 abnormal and 8 normal cases. The best performance is achieved for the linear discriminant and support vector machine classifiers, with an area under the ROC curve (A(z)) of 0.78. Separate ROC curves are built for classification of abnormalities of different degrees of subtlety versus normal class. Here the best performance in terms of A(z) is 0.90 for differentiation between obviously abnormal and normal pixels. The system is compared with two human observers, an expert chest radiologist and a chest radiologist in training, on evaluation of regions. Each lung field is divided in four regions, and the reference standard and the probability maps are converted into region score

关键词： computer-aided detection interstitial lung disease chest radiography texture analysis

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computer-aided detection of clustered microcalcifications in digital breast tomosynthesis: A 3D approach

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MEDICAL PHYSICS 2012年第1期39卷 28-39页

作者： Sahiner, Berkman Chan, Heang-Ping Hadjiiski, Lubomir M. Helvie, Mark A. Wei, Jun Zhou, Chuan Lu, Yao Univ Michigan Dept Radiol Ann Arbor MI 48109 USA

Purpose: To design a computer-aided detection (CADe) system for clustered microcalcifications in reconstructed digital breast tomosynthesis (DBT) volumes and to perform a preliminary evaluation of the CADe system. Methods: IRB approval and informed consent were obtained in this study. A data set of two-view DBT of 72 breasts containing microcalcification clusters was collected from 72 subjects who were scheduled to undergo breast biopsy. Based on tissue sampling results, 17 cases had breast cancer and 55 were benign. A separate data set of two-view DBT of 38 breasts free of clustered microcalcifications from 38 subjects was collected to independently estimate the number of false-positives (FPs) generated by the CADe system. A radiologist experienced in breast imaging marked the biopsied cluster of microcalcifications with a 3D bounding box using all available clinical and imaging information. A CADe system was designed to detect microcalcification clusters in the reconstructed volume. The system consisted of prescreening, clustering, and false-positive reduction stages. In the prescreening stage, the conspicuity of microcalcification-like objects was increased by an enhancement-modulated 3D calcification response function. An iterative thresholding and 3D object growing method was used to detect cluster seed objects, which were used as potential centers of microcalcification clusters. In the cluster detection stage, microcalcification candidates were identified using a second iterative thresholding procedure, which was applied to the signal-to-noise ratio (SNR) enhanced image voxels with a positive calcification response. Starting with each cluster seed object as the initial cluster center, a dynamic clustering algorithm formed a cluster candidate by including microcalcification candidates within a 3D neighborhood of the cluster seed object that satisfied the clustering criteria. The number, size, and SNR of the microcalcifications in a cluster candidate and the clu

关键词： digital breast tomosynthesis computer-aided detection microcalcification

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computer-aided detection improves brain metastasis identification on non-enhanced CT in less experienced radiologists

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ACTA RADIOLOGICA 2023年第5期64卷 1958-1965页

作者： Kato, Shimpei Amemiya, Shiori Takao, Hidemasa Yamashita, Hiroshi Sakamoto, Naoya Miki, Soichiro Watanabe, Yusuke Suzuki, Fumio Fujimoto, Kotaro Mizuki, Masumi Abe, Osamu Univ Tokyo Grad Sch Med Dept Radiol 7-3-1 Hongo Bunkyo Ku Tokyo 138655 Japan Teikyo Univ Hosp Dept Radiol Kawasaki Kanagawa Japan

Background Brain metastases (BMs) are the most common intracranial tumors causing neurological complications associated with significant morbidity and mortality. Purpose To evaluate the effect of computer-aided detection (CAD) on the performance of observers in detecting BMs on non-enhanced computed tomography (NECT). Material and Methods Three less experienced and three experienced radiologists interpreted 30 NECT scans with 89 BMs in 25 cases to detect BMs with and without the assistance of CAD. The observers' sensitivity, number of false positives (FPs), positive predictive value (PPV), and reading time with and without CAD were compared using paired t-tests. The sensitivity of CAD and the observers were compared using a one-sample t-test Results With CAD, less experienced radiologists' sensitivity significantly increased from 27.7% +/- 4.6% to 32.6% +/- 4.8% (P = 0.007), while the experienced radiologists' sensitivity did not show a significant difference (from 33.3% +/- 3.5% to 31.9% +/- 3.7%;P = 0.54). There was no significant difference between conditions with CAD and without CAD for FPs (less experienced radiologists: 23.0 +/- 10.4 and 25.0 +/- 9.3;P = 0.32;experienced radiologists: 18.3 +/- 7.4 and 17.3 +/- 6.7;P = 0.76) and PPVs (less experienced radiologists: 57.9% +/- 8.3% and 50.9% +/- 7.0%;P = 0.14;experienced radiologists: 61.8% +/- 12.7% and 64.0% +/- 12.1%;P = 0.69). There were no significant differences in reading time with and without CAD (85.0 +/- 45.6 s and 73.7 +/- 36.7 s;P = 0.09). The sensitivity of CAD was 47.2% (with a PPV of 8.9%), which was significantly higher than that of any radiologist (P < 0.001). Conclusion CAD improved BM detection sensitivity on NECT without increasing FPs or reading time among less experienced radiologists, but this was not the case among experienced radiologists.

关键词： computer-aided detection deep learning brain metastases computed tomography

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computer-aided detection and diagnosis for prostate cancer based on mono and multi-parametric MRI: A review

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computerS IN BIOLOGY AND MEDICINE 2015年 60卷 8-31页

作者： Lemaitre, Guillaume Marti, Robert Freixenet, Jordi Vilanova, Joan C. Walker, Paul M. Meriaudeau, Fabrice Univ Bourgogne LE2I UMR CNRS 6306 F-71200 Le Creusot France Univ Bourgogne LE2I UMR CNRS 6306 F-21000 Dijon France Univ Girona ViCOROB Girona 17071 Spain Clin Girona Dept Magnet Resonance Girona 17002 Spain

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. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： computer-aided detection computer-aided Diagnosis Prostate cancer Magnetic Resonance Imaging Magnetic Resonance Spectroscopy Imaging

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