1
|
Zou Y, Chen S, Che C, Zhang J, Zhang Q. Breast cancer histopathology image classification based on dual-stream high-order network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
|
2
|
Shih CC, Chen SH, Chen GD, Chang CC, Shih YL. Development of a Longitudinal Diagnosis and Prognosis in Patients with Chronic Kidney Disease: Intelligent Clinical Decision-Making Scheme. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312807. [PMID: 34886533 PMCID: PMC8657318 DOI: 10.3390/ijerph182312807] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 01/08/2023]
Abstract
Previous studies on CKD patients have mostly been retrospective, cross-sectional studies. Few studies have assessed the longitudinal assessment of patients over an extended period. In consideration of the heterogeneity of CKD progression. It’s critical to develop a longitudinal diagnosis and prognosis for CKD patients. We proposed an auto Machine Learning (ML) scheme in this study. It consists of four main parts: classification pipeline, cross-validation (CV), Taguchi method and improve strategies. This study includes datasets from 50,174 patients, data were collected from 32 chain clinics and three special physical examination centers, between 2015 and 2019. The proposed auto-ML scheme can auto-select the level of each strategy to associate with a classifier which finally shows an acceptable testing accuracy of 86.17%, balanced accuracy of 84.08%, sensitivity of 90.90% and specificity of 77.26%, precision of 88.27%, and F1 score of 89.57%. In addition, the experimental results showed that age, creatinine, high blood pressure, smoking are important risk factors, and has been proven in previous studies. Our auto-ML scheme light on the possibility of evaluation for the effectiveness of one or a combination of those risk factors. This methodology may provide essential information and longitudinal change for personalized treatment in the future.
Collapse
Affiliation(s)
- Chin-Chuan Shih
- Dean of the Lian-An Clinic, Taipei 24200, Taiwan;
- Deputy Chairman, Taiwan Association of Family Medicine, Taipei 24200, Taiwan
| | - Ssu-Han Chen
- Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan;
- Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan
| | - Gin-Den Chen
- Institute of Medicine, Chung Shan Medical University, Taichung 40201, Taiwan;
| | - Chi-Chang Chang
- Department of Medical Informatics, Chung Shan Medical University & IT Office, Chung Shan Medical University Hospital, Taichung 40201, Taiwan
- Department of Information Management, Ming Chuan University, Taoyuan 33300, Taiwan
- Correspondence: ; Tel.: +886-4-24730022
| | - Yu-Lin Shih
- Department of Otolaryngology-Head and Neck Surgery, Chang-Gung Memorial Hospital, Linkou Branch, Taoyuan City 33305, Taiwan;
| |
Collapse
|
3
|
Shaikh TA, Ali R. An intelligent healthcare system for optimized breast cancer diagnosis using harmony search and simulated annealing (HS-SA) algorithm. INFORMATICS IN MEDICINE UNLOCKED 2020. [DOI: 10.1016/j.imu.2020.100408] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022] Open
|
4
|
Mao N, Yin P, Wang Q, Liu M, Dong J, Zhang X, Xie H, Hong N. Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study. J Am Coll Radiol 2019; 16:485-491. [DOI: 10.1016/j.jacr.2018.09.041] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2018] [Accepted: 09/14/2018] [Indexed: 01/22/2023]
|
5
|
Artificial Intelligence for Breast MRI in 2008-2018: A Systematic Mapping Review. AJR Am J Roentgenol 2019; 212:280-292. [PMID: 30601029 DOI: 10.2214/ajr.18.20389] [Citation(s) in RCA: 36] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
OBJECTIVE The purpose of this study is to review literature from the past decade on applications of artificial intelligence (AI) to breast MRI. MATERIALS AND METHODS In June 2018, a systematic search of the literature was performed to identify articles on the use of AI in breast MRI. For each article identified, the surname of the first author, year of publication, journal of publication, Web of Science Core Collection journal category, country of affiliation of the first author, study design, dataset, study aim(s), AI methods used, and, when available, diagnostic performance were recorded. RESULTS Sixty-seven studies, 58 (87%) of which had a retrospective design, were analyzed. When journal categories were considered, 36% of articles were identified as being included in the radiology and imaging journal category. Contrast-enhanced sequences were used for most AI applications (n = 50; 75%) and, on occasion, were combined with other MRI sequences (n = 8; 12%). Four main clinical aims were addressed: breast lesion classification (n = 36; 54%), image processing (n = 14; 21%), prognostic imaging (n = 9; 13%), and response to neoadjuvant therapy (n = 8; 12%). Artificial neural networks, support vector machines, and clustering were the most frequently used algorithms, accounting for 66%. The performance achieved and the most frequently used techniques were then analyzed according to specific clinical aims. Supervised learning algorithms were primarily used for lesion characterization, with the AUC value from ROC analysis ranging from 0.74 to 0.98 (median, 0.87) and with that from prognostic imaging ranging from 0.62 to 0.88 (median, 0.80), whereas unsupervised learning was mainly used for image processing purposes. CONCLUSION Interest in the application of advanced AI methods to breast MRI is growing worldwide. Although this growth is encouraging, the current performance of AI applications in breast MRI means that such applications are still far from being incorporated into clinical practice.
Collapse
|
6
|
Computerized Image Analysis to Differentiate Benign and Malignant Breast Tumors on Magnetic Resonance Diffusion Weighted Image. J Comput Assist Tomogr 2019; 43:93-97. [DOI: 10.1097/rct.0000000000000793] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
|
7
|
Henni K, Mezghani N, Gouin-Vallerand C, Ruer P, Ouakrim Y, Vallières É. Feature selection for driving fatigue characterization and detection using visual- and signal-based sensors. ACTA ACUST UNITED AC 2018. [DOI: 10.1186/s40535-018-0054-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
8
|
Computer-Aided Diagnosis Scheme for Distinguishing Between Benign and Malignant Masses in Breast DCE-MRI. J Digit Imaging 2018; 29:388-93. [PMID: 26691512 DOI: 10.1007/s10278-015-9856-7] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Our purpose in this study was to develop a computer-aided diagnosis (CAD) scheme for distinguishing between benign and malignant breast masses in dynamic contrast material-enhanced magnetic resonance imaging (DCE-MRI). Our database consisted 90 DCE-MRI examinations, each of which contained four sequential phase images; this database included 28 benign masses and 62 malignant masses. In our CAD scheme, we first determined 11 objective features of masses by taking into account the image features and the dynamic changes in signal intensity that experienced radiologists commonly use for describing masses in DCE-MRI. Quadratic discriminant analysis (QDA) was employed to distinguish between benign and malignant masses. As the input of the QDA, a combination of four objective features was determined among the 11 objective features according to a stepwise method. These objective features were as follows: (i) the change in signal intensity from 2 to 5 min; (ii) the change in signal intensity from 0 to 2 min; (iii) the irregularity of the shape; and (iv) the smoothness of the margin. Using this approach, the classification accuracy, sensitivity, and specificity were shown to be 85.6 % (77 of 90), 87.1 % (54 of 62), and 82.1 % (23 of 28), respectively. Furthermore, the positive and negative predictive values were 91.5 % (54 of 59) and 74.2 % (23 of 31), respectively. Our CAD scheme therefore exhibits high classification accuracy and is useful in the differential diagnosis of masses in DCE-MRI images.
Collapse
|
9
|
Yin XX, Hadjiloucas S, Chen JH, Zhang Y, Wu JL, Su MY. Tensor based multichannel reconstruction for breast tumours identification from DCE-MRIs. PLoS One 2017; 12:e0172111. [PMID: 28282379 PMCID: PMC5345763 DOI: 10.1371/journal.pone.0172111] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Accepted: 01/31/2017] [Indexed: 11/18/2022] Open
Abstract
A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms.
Collapse
Affiliation(s)
- X. -X. Yin
- Centre for Applied Informatics School of Engineering and Science, Victoria University, Melbourne, Australia
- * E-mail: (XXY); (YZ); (JLW)
| | - S. Hadjiloucas
- School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, United Kingdom
| | - J. -H. Chen
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America
- Department of Radiology, EDa Hospital and I-Shou University, Kaohsiung, Taiwan
| | - Y. Zhang
- Centre for Applied Informatics School of Engineering and Science, Victoria University, Melbourne, Australia
- School of Computer Science, Fudan University, China
- * E-mail: (XXY); (YZ); (JLW)
| | - J. -L. Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China
- * E-mail: (XXY); (YZ); (JLW)
| | - M. -Y. Su
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, United States of America
| |
Collapse
|
10
|
Yin XX, Hadjiloucas S, Zhang Y, Su MY, Miao Y, Abbott D. Pattern identification of biomedical images with time series: Contrasting THz pulse imaging with DCE-MRIs. Artif Intell Med 2016; 67:1-23. [PMID: 26951630 DOI: 10.1016/j.artmed.2016.01.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2015] [Revised: 12/28/2015] [Accepted: 01/16/2016] [Indexed: 12/25/2022]
Abstract
OBJECTIVE We provide a survey of recent advances in biomedical image analysis and classification from emergent imaging modalities such as terahertz (THz) pulse imaging (TPI) and dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) and identification of their underlining commonalities. METHODS Both time and frequency domain signal pre-processing techniques are considered: noise removal, spectral analysis, principal component analysis (PCA) and wavelet transforms. Feature extraction and classification methods based on feature vectors using the above processing techniques are reviewed. A tensorial signal processing de-noising framework suitable for spatiotemporal association between features in MRI is also discussed. VALIDATION Examples where the proposed methodologies have been successful in classifying TPIs and DCE-MRIs are discussed. RESULTS Identifying commonalities in the structure of such heterogeneous datasets potentially leads to a unified multi-channel signal processing framework for biomedical image analysis. CONCLUSION The proposed complex valued classification methodology enables fusion of entire datasets from a sequence of spatial images taken at different time stamps; this is of interest from the viewpoint of inferring disease proliferation. The approach is also of interest for other emergent multi-channel biomedical imaging modalities and of relevance across the biomedical signal processing community.
Collapse
Affiliation(s)
- Xiao-Xia Yin
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
| | - Sillas Hadjiloucas
- School of Systems Engineering and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK
| | - Yanchun Zhang
- Centre for Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Min-Ying Su
- Tu & Yuen Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, Irvine, CA, USA
| | - Yuan Miao
- College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia
| | - Derek Abbott
- Centre for Biomedical Engineering (CBME) and School of Electrical & Electronic Engineering, The University of Adelaide, South Australia, SA 5000, Australia
| |
Collapse
|
11
|
Khalifa F, Soliman A, El-Baz A, Abou El-Ghar M, El-Diasty T, Gimel'farb G, Ouseph R, Dwyer AC. Models and methods for analyzing DCE-MRI: a review. Med Phys 2015; 41:124301. [PMID: 25471985 DOI: 10.1118/1.4898202] [Citation(s) in RCA: 197] [Impact Index Per Article: 21.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
PURPOSE To present a review of most commonly used techniques to analyze dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), discusses their strengths and weaknesses, and outlines recent clinical applications of findings from these approaches. METHODS DCE-MRI allows for noninvasive quantitative analysis of contrast agent (CA) transient in soft tissues. Thus, it is an important and well-established tool to reveal microvasculature and perfusion in various clinical applications. In the last three decades, a host of nonparametric and parametric models and methods have been developed in order to quantify the CA's perfusion into tissue and estimate perfusion-related parameters (indexes) from signal- or concentration-time curves. These indexes are widely used in various clinical applications for the detection, characterization, and therapy monitoring of different diseases. RESULTS Promising theoretical findings and experimental results for the reviewed models and techniques in a variety of clinical applications suggest that DCE-MRI is a clinically relevant imaging modality, which can be used for early diagnosis of different diseases, such as breast and prostate cancer, renal rejection, and liver tumors. CONCLUSIONS Both nonparametric and parametric approaches for DCE-MRI analysis possess the ability to quantify tissue perfusion.
Collapse
Affiliation(s)
- Fahmi Khalifa
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292 and Electronics and Communication Engineering Department, Mansoura University, Mansoura 35516, Egypt
| | - Ahmed Soliman
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Ayman El-Baz
- BioImaging Laboratory, Department of Bioengineering, University of Louisville, Louisville, Kentucky 40292
| | - Mohamed Abou El-Ghar
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Tarek El-Diasty
- Radiology Department, Urology and Nephrology Center, Mansoura University, Mansoura 35516, Egypt
| | - Georgy Gimel'farb
- Department of Computer Science, University of Auckland, Auckland 1142, New Zealand
| | - Rosemary Ouseph
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
| | - Amy C Dwyer
- Kidney Transplantation-Kidney Disease Center, University of Louisville, Louisville, Kentucky 40202
| |
Collapse
|
12
|
Alic L, Niessen WJ, Veenland JF. Quantification of heterogeneity as a biomarker in tumor imaging: a systematic review. PLoS One 2014; 9:e110300. [PMID: 25330171 PMCID: PMC4203782 DOI: 10.1371/journal.pone.0110300] [Citation(s) in RCA: 110] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2014] [Accepted: 09/15/2014] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Many techniques are proposed for the quantification of tumor heterogeneity as an imaging biomarker for differentiation between tumor types, tumor grading, response monitoring and outcome prediction. However, in clinical practice these methods are barely used. This study evaluates the reported performance of the described methods and identifies barriers to their implementation in clinical practice. METHODOLOGY The Ovid, Embase, and Cochrane Central databases were searched up to 20 September 2013. Heterogeneity analysis methods were classified into four categories, i.e., non-spatial methods (NSM), spatial grey level methods (SGLM), fractal analysis (FA) methods, and filters and transforms (F&T). The performance of the different methods was compared. PRINCIPAL FINDINGS Of the 7351 potentially relevant publications, 209 were included. Of these studies, 58% reported the use of NSM, 49% SGLM, 10% FA, and 28% F&T. Differentiation between tumor types, tumor grading and/or outcome prediction was the goal in 87% of the studies. Overall, the reported area under the curve (AUC) ranged from 0.5 to 1 (median 0.87). No relation was found between the performance and the quantification methods used, or between the performance and the imaging modality. A negative correlation was found between the tumor-feature ratio and the AUC, which is presumably caused by overfitting in small datasets. Cross-validation was reported in 63% of the classification studies. Retrospective analyses were conducted in 57% of the studies without a clear description. CONCLUSIONS In a research setting, heterogeneity quantification methods can differentiate between tumor types, grade tumors, and predict outcome and monitor treatment effects. To translate these methods to clinical practice, more prospective studies are required that use external datasets for validation: these datasets should be made available to the community to facilitate the development of new and improved methods.
Collapse
Affiliation(s)
- Lejla Alic
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Department of Intelligent Imaging, Netherlands Organization for Applied Scientific Research (TNO), The Hague, The Netherlands
| | - Wiro J. Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
- Imaging Physics, Faculty of Applied Sciences, Delft University of Technology, Delft, The Netherlands
| | - Jifke F. Veenland
- Biomedical Imaging Group Rotterdam, Department of Radiology and Medical Informatics, Erasmus Medical Center Rotterdam, Rotterdam, The Netherlands
| |
Collapse
|
13
|
Mazurowski MA, Zhang J, Grimm LJ, Yoon SC, Silber JI. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273:365-72. [PMID: 25028781 DOI: 10.1148/radiol.14132641] [Citation(s) in RCA: 166] [Impact Index Per Article: 16.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
PURPOSE To investigate associations between breast cancer molecular subtype and semiautomatically extracted magnetic resonance (MR) imaging features. MATERIALS AND METHODS Imaging and genomic data from the Cancer Genome Atlas and the Cancer Imaging Archive for 48 patients with breast cancer from four institutions in the United States were used in this institutional review board approval-exempt study. Computer vision algorithms were applied to extract 23 imaging features from lesions indicated by a breast radiologist on MR images. Morphologic, textural, and dynamic features were extracted. Molecular subtype was determined on the basis of genomic analysis. Associations between the imaging features and molecular subtype were evaluated by using logistic regression and likelihood ratio tests. The analysis controlled for the age of the patients, their menopausal status, and the orientation of the MR images (sagittal vs axial). RESULTS There is an association (P = .0015) between the luminal B subtype and a dynamic contrast material-enhancement feature that quantifies the relationship between lesion enhancement and background parenchymal enhancement. Cancers with a higher ratio of lesion enhancement rate to background parenchymal enhancement rate are more likely to be luminal B subtype. CONCLUSION The luminal B subtype of breast cancer is associated with MR imaging features that relate the enhancement dynamics of the tumor and the background parenchyma.
Collapse
Affiliation(s)
- Maciej A Mazurowski
- From the Department of Radiology, Duke University Medical Center, 2301 Erwin Rd, Box 3808, Durham, NC 27710 (M.A.M., J.Z., L.J.G., S.C.Y.); and Department of Biomedical Engineering, Duke University, Pratt School of Engineering, Durham, NC (J.I.S.)
| | | | | | | | | |
Collapse
|
14
|
Edwards SD, Lipson JA, Ikeda DM, Lee JM. Updates and revisions to the BI-RADS magnetic resonance imaging lexicon. Magn Reson Imaging Clin N Am 2013; 21:483-93. [PMID: 23928239 DOI: 10.1016/j.mric.2013.02.005] [Citation(s) in RCA: 50] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
This article summarizes the updates and revisions to the second edition of the BI-RADS MRI lexicon. A new feature in the lexicon is background parenchymal enhancement and its descriptors. Another major focus is on revised terminology for masses and non-mass enhancement. A section on breast implants and associated lexicon terms has also been added. Because diagnostic breast imaging increasingly includes multimodality evaluation, the new edition of the lexicon also contains revised recommendations for combined reporting with mammography and ultrasound if these modalities are included as comparison, and clarification on the use of final assessment categories in MR imaging.
Collapse
Affiliation(s)
- Sonya D Edwards
- Department of Radiology, Stanford Comprehensive Cancer Center, Stanford University Medical Center, Stanford, CA 94305, USA
| | | | | | | |
Collapse
|
15
|
Giger ML, Karssemeijer N, Schnabel JA. Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer. Annu Rev Biomed Eng 2013; 15:327-57. [PMID: 23683087 DOI: 10.1146/annurev-bioeng-071812-152416] [Citation(s) in RCA: 117] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The role of breast image analysis in radiologists' interpretation tasks in cancer risk assessment, detection, diagnosis, and treatment continues to expand. Breast image analysis methods include segmentation, feature extraction techniques, classifier design, biomechanical modeling, image registration, motion correction, and rigorous methods of evaluation. We present a review of the current status of these task-based image analysis methods, which are being developed for the various image acquisition modalities of mammography, tomosynthesis, computed tomography, ultrasound, and magnetic resonance imaging. Depending on the task, image-based biomarkers from such quantitative image analysis may include morphological, textural, and kinetic characteristics and may depend on accurate modeling and registration of the breast images. We conclude with a discussion of future directions.
Collapse
Affiliation(s)
- Maryellen L Giger
- Department of Radiology, University of Chicago, Chicago, IL 60637, USA.
| | | | | |
Collapse
|