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Homssi M, Sweeney EM, Demmon E, Mannheim W, Sakirsky M, Wang Y, Gauthier SA, Gupta A, Nguyen TD. Evaluation of the Statistical Detection of Change Algorithm for Screening Patients with MS with New Lesion Activity on Longitudinal Brain MRI. AJNR Am J Neuroradiol 2023; 44:649-655. [PMID: 37142431 PMCID: PMC10249703 DOI: 10.3174/ajnr.a7858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 04/03/2023] [Indexed: 05/06/2023]
Abstract
BACKGROUND AND PURPOSE Identification of new MS lesions on longitudinal MR imaging by human readers is time-consuming and prone to error. Our objective was to evaluate the improvement in the performance of subject-level detection by readers when assisted by the automated statistical detection of change algorithm. MATERIALS AND METHODS A total of 200 patients with MS with a mean interscan interval of 13.2 (SD, 2.4) months were included. Statistical detection of change was applied to the baseline and follow-up FLAIR images to detect potential new lesions for confirmation by readers (Reader + statistical detection of change method). This method was compared with readers operating in the clinical workflow (Reader method) for a subject-level detection of new lesions. RESULTS Reader + statistical detection of change found 30 subjects (15.0%) with at least 1 new lesion, while Reader detected 16 subjects (8.0%). As a subject-level screening tool, statistical detection of change achieved a perfect sensitivity of 1.00 (95% CI, 0.88-1.00) and a moderate specificity of 0.67 (95% CI, 0.59-0.74). The agreement on a subject level was 0.91 (95% CI, 0.87-0.95) between Reader + statistical detection of change and Reader, and 0.72 (95% CI, 0.66-0.78) between Reader + statistical detection of change and statistical detection of change. CONCLUSIONS The statistical detection of change algorithm can serve as a time-saving screening tool to assist human readers in verifying 3D FLAIR images of patients with MS with suspected new lesions. Our promising results warrant further evaluation of statistical detection of change in prospective multireader clinical studies.
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Affiliation(s)
- M Homssi
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
| | - E M Sweeney
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE) Center, Department of Biostatistics, Epidemiology, and Informatics (E.M.S.), University of Pennsylvania, Philadelphia, Pennsylvania
| | - E Demmon
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
| | - W Mannheim
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
| | - M Sakirsky
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
| | - Y Wang
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
| | - S A Gauthier
- Department of Neurology (E.D., W.M., M.S., S.A.G.)
- The Feil Family Brain & Mind Institute (S.A.G.), Weill Cornell Medicine, New York, New York
| | - A Gupta
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
| | - T D Nguyen
- From the Department of Radiology (M.H., Y.W., A.G., T.D.N.)
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Stember JN, Young RJ, Shalu H. Direct Evaluation of Treatment Response in Brain Metastatic Disease with Deep Neuroevolution. J Digit Imaging 2023; 36:536-546. [PMID: 36396839 PMCID: PMC10039135 DOI: 10.1007/s10278-022-00725-5] [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: 04/06/2022] [Revised: 09/29/2022] [Accepted: 10/25/2022] [Indexed: 11/18/2022] Open
Abstract
Cancer centers have an urgent and unmet clinical and research need for AI that can guide patient management. A core component of advancing cancer treatment research is assessing response to therapy. Doing so by hand, for example, as per RECIST or RANO criteria, is tedious and time-consuming, and can miss important tumor response information. Most notably, the prevalent response criteria often exclude lesions, the non-target lesions, altogether. We wish to assess change in a holistic fashion that includes all lesions, obtaining simple, informative, and automated assessments of tumor progression or regression. Because genetic sub-types of cancer can be fairly specific and patient enrollment in therapy trials is often limited in number and accrual rate, we wish to make response assessments with small training sets. Deep neuroevolution (DNE) is a novel radiology artificial intelligence (AI) optimization approach that performs well on small training sets. Here, we use a DNE parameter search to optimize a convolutional neural network (CNN) that predicts progression versus regression of metastatic brain disease. We analyzed 50 pairs of MRI contrast-enhanced images as our training set. Half of these pairs, separated in time, qualified as disease progression, while the other 25 image pairs constituted regression. We trained the parameters of a CNN via "mutations" that consisted of random CNN weight adjustments and evaluated mutation "fitness" as summed training set accuracy. We then incorporated the best mutations into the next generation's CNN, repeating this process for approximately 50,000 generations. We applied the CNNs to our training set, as well as a separate testing set with the same class balance of 25 progression and 25 regression cases. DNE achieved monotonic convergence to 100% training set accuracy. DNE also converged monotonically to 100% testing set accuracy. We have thus shown that DNE can accurately classify brain metastatic disease progression versus regression. Future work will extend the input from 2D image slices to full 3D volumes, and include the category of "no change." We believe that an approach such as ours can ultimately provide a useful and informative complement to RANO/RECIST assessment and volumetric AI analysis.
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Affiliation(s)
- Joseph N Stember
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA.
| | - Robert J Young
- Memorial Sloan Kettering Cancer Center, 1275 York Avenue, NY, NY, 10065, USA
| | - Hrithwik Shalu
- Indian Institute of Technology Madras, Madras, Chennai, 600036, India
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3
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Rovira A, Corral JF, Auger C, Valverde S, Vidal-Jordana A, Oliver A, de Barros A, Ng Wong YK, Tintoré M, Pareto D, Aymerich FX, Montalban X, Lladó X, Alonso J. Assessment of automatic decision-support systems for detecting active T2 lesions in multiple sclerosis patients. Mult Scler 2021; 28:1209-1218. [PMID: 34859704 DOI: 10.1177/13524585211061339] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Active (new/enlarging) T2 lesion counts are routinely used in the clinical management of multiple sclerosis. Thus, automated tools able to accurately identify active T2 lesions would be of high interest to neuroradiologists for assisting in their clinical activity. OBJECTIVE To compare the accuracy in detecting active T2 lesions and of radiologically active patients based on different visual and automated methods. METHODS One hundred multiple sclerosis patients underwent two magnetic resonance imaging examinations within 12 months. Four approaches were assessed for detecting active T2 lesions: (1) conventional neuroradiological reports; (2) prospective visual analyses performed by an expert; (3) automated unsupervised tool; and (4) supervised convolutional neural network. As a gold standard, a reference outcome was created by the consensus of two observers. RESULTS The automated methods detected a higher number of active T2 lesions, and a higher number of active patients, but a higher number of false-positive active patients than visual methods. The convolutional neural network model was more sensitive in detecting active T2 lesions and active patients than the other automated method. CONCLUSION Automated convolutional neural network models show potential as an aid to neuroradiological assessment in clinical practice, although visual supervision of the outcomes is still required.
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Affiliation(s)
- Alex Rovira
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain/Neuroradiology Research Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Juan Francisco Corral
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain/Neuroradiology Research Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Cristina Auger
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain/Neuroradiology Research Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Sergi Valverde
- TensorMedical, Girona, Spain/Department of Computer Architecture and Technology, University of Girona, Girona, Spain
| | - Angela Vidal-Jordana
- Department of Neurology and Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron University Hospital, Barcelona, Spain/Clinical Neuroimmunology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Arnau Oliver
- Department of Computer Architecture and Technology, University of Girona, Girona, Spain
| | - Andrea de Barros
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain
| | - Yiken Karelys Ng Wong
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain
| | - Mar Tintoré
- Department of Neurology and Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron University Hospital, Barcelona, Spain/Clinical Neuroimmunology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Deborah Pareto
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain/Neuroradiology Research Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain
| | - Francesc Xavier Aymerich
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain/Neuroradiology Research Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain/Automatic Control Department, Universitat Politècnica de Catalunya BarcelonaTech, Barcelona, Spain
| | - Xavier Montalban
- Department of Neurology and Neuroimmunology, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron University Hospital, Barcelona, Spain/Clinical Neuroimmunology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Xavier Lladó
- Department of Computer Architecture and Technology, University of Girona, Girona, Spain
| | - Juli Alonso
- Neuroradiology Section, Department of Radiology (IDI), Vall d'Hebron University Hospital, Barcelona, Spain/Neuroradiology Research Group, Vall d'Hebron Research Institute (VHIR), Barcelona, Spain/Universitat Autònoma de Barcelona, Barcelona, Spain
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Kuhn MJ, Patriarche JW, Patriarche D, Kirchin MA, Bona M, Pirovano G. The TRUTH confirmed: validation of an intraindividual comparison of gadobutrol and gadoteridol for imaging of glioblastoma using quantitative enhancement analysis. Eur Radiol Exp 2021; 5:46. [PMID: 34635965 PMCID: PMC8505590 DOI: 10.1186/s41747-021-00240-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 08/06/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Previous intraindividual comparative studies evaluating gadobutrol and gadoteridol for contrast-enhanced magnetic resonance imaging (MRI) of brain tumours have relied on subjective image assessment, potentially leading to misleading conclusions. We used artificial intelligence algorithms to objectively compare the enhancement achieved with these contrast agents in glioblastoma patients. METHODS Twenty-seven patients from a prior study who received identical doses of 0.1 mmol/kg gadobutrol and gadoteridol (with appropriate washout in between) were evaluated. Quantitative enhancement (QE) maps of the normalised enhancement of voxels, derived from computations based on the comparison of contrast-enhanced T1-weighted images relative to the harmonised intensity on unenhanced T1-weighted images, were compared. Bland-Altman analysis, linear regression analysis and Pearson correlation coefficient (r) determination were performed to compare net QE and per-region of interest (per-ROI) average QE (net QE divided by the number of voxels). RESULTS No significant differences were observed for comparisons performed on net QE (mean difference -24.37 ± 620.8, p = 0.840, r = 0.989) or per-ROI average QE (0.0043 ± 0.0218, p = 0.313, r = 0.958). Bland-Altman analysis revealed better per-ROI average QE for gadoteridol-enhanced MRI in 19/27 (70.4%) patients although the mean difference (0.0043) was close to zero indicating high concordance and the absence of fixed bias. CONCLUSIONS The enhancement of glioblastoma achieved with gadoteridol and gadobutrol at 0.1 mmol/kg bodyweight is similar indicating that these agents have similar contrast efficacy and can be used interchangeably, confirming the results of a prior double-blind, randomised, intraindividual, crossover study.
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Affiliation(s)
- Matthew J Kuhn
- University of Illinois College of Medicine at Peoria, 221 NE Glen Oak Ave, Peoria, IL, 61636, USA. .,A.I. Analysis, Inc., 1425 Broadway #20-2656, Seattle, WA, 98122, USA.
| | | | | | - Miles A Kirchin
- Global Medical & Regulatory Affairs, Bracco Imaging SpA, Via Caduti di Marcinelle, 13, 20134, Milan, Italy
| | - Massimo Bona
- Global Medical & Regulatory Affairs, Bracco Imaging SpA, Via Caduti di Marcinelle, 13, 20134, Milan, Italy
| | - Gianpaolo Pirovano
- Global Medical & Regulatory Affairs, Bracco Diagnostics, Inc., 259 Prospect Plains Rd. Building H, Monroe Township, NJ, 08831, USA
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5
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Shearkhani O, Khademi A, Eilaghi A, Hojjat SP, Symons SP, Heyn C, Machnowska M, Chan A, Sahgal A, Maralani PJ. Detection of Volume-Changing Metastatic Brain Tumors on Longitudinal MRI Using a Semiautomated Algorithm Based on the Jacobian Operator Field. AJNR Am J Neuroradiol 2017; 38:2059-2066. [PMID: 28882862 DOI: 10.3174/ajnr.a5352] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Accepted: 06/15/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Accurate follow-up of metastatic brain tumors has important implications for patient prognosis and management. The aim of this study was to develop and evaluate the accuracy of a semiautomated algorithm in detecting growing or shrinking metastatic brain tumors on longitudinal brain MRIs. MATERIALS AND METHODS We used 50 pairs of successive MR imaging datasets, 30 on 1.5T and 20 on 3T, containing contrast-enhanced 3D T1-weighted sequences. These yielded 150 growing or shrinking metastatic brain tumors. To detect them, we completed 2 major steps: 1) spatial normalization and calculation of the Jacobian operator field to quantify changes between scans, and 2) metastatic brain tumor candidate segmentation and detection of volume-changing metastatic brain tumors with the Jacobian operator field. Receiver operating characteristic analysis was used to assess the detection accuracy of the algorithm, and it was verified with jackknife resampling. The reference standard was based on detections by a neuroradiologist. RESULTS The areas under the receiver operating characteristic curves were 0.925 for 1.5T and 0.965 for 3T. Furthermore, at its optimal performance, the algorithm achieved a sensitivity of 85.1% and 92.1% and specificity of 86.7% and 91.3% for 1.5T and 3T, respectively. Vessels were responsible for most false-positives. Newly developed or resolved metastatic brain tumors were a major source of false-negatives. CONCLUSIONS The proposed algorithm could detect volume-changing metastatic brain tumors on longitudinal brain MRIs with statistically high accuracy, demonstrating its potential as a computer-aided change-detection tool for complementing the performance of radiologists, decreasing inter- and intraobserver variability, and improving efficacy.
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Affiliation(s)
- O Shearkhani
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - A Khademi
- Department of Biomedical Engineering (A.K.), Ryerson University, Toronto, Ontario, Canada
| | - A Eilaghi
- Mechanical Engineering Department (A.E.), Australian College of Kuwait, Kuwait City, Kuwait
| | - S-P Hojjat
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - S P Symons
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - C Heyn
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - M Machnowska
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - A Chan
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
| | - A Sahgal
- Radiation Oncology (A.S.), University of Toronto, Toronto, Ontario, Canada
| | - P J Maralani
- From the Departments of Medical Imaging (O.S., S.-P.H., S.P.S., C.H., M.M., A.C., P.J.M.)
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Validation of White-Matter Lesion Change Detection Methods on a Novel Publicly Available MRI Image Database. Neuroinformatics 2016; 14:403-20. [DOI: 10.1007/s12021-016-9301-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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7
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Bisdas S, Smrdel U, Bajrovic FF, Surlan-Popovic K. Assessment of Progression-Free-Survival in Glioblastomas by Intratreatment Dynamic Contrast-Enhanced MRI. Clin Neuroradiol 2014; 26:39-45. [DOI: 10.1007/s00062-014-0328-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2013] [Accepted: 07/29/2014] [Indexed: 10/24/2022]
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8
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Detection of time-varying structures by large deformation diffeomorphic metric mapping to aid reading of high-resolution CT images of the lung. PLoS One 2014; 9:e85580. [PMID: 24454894 PMCID: PMC3890326 DOI: 10.1371/journal.pone.0085580] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2013] [Accepted: 11/28/2013] [Indexed: 12/21/2022] Open
Abstract
Objectives To evaluate the accuracy of advanced non-linear registration of serial lung Computed Tomography (CT) images using Large Deformation Diffeomorphic Metric Mapping (LDDMM). Methods Fifteen cases of lung cancer with serial lung CT images (interval: 62.2±26.9 days) were used. After affine transformation, three dimensional, non-linear volume registration was conducted using LDDMM with or without cascading elasticity control. Registration accuracy was evaluated by measuring the displacement of landmarks placed on vessel bifurcations for each lung segment. Subtraction images and Jacobian color maps, calculated from the transformation matrix derived from image warping, were generated, which were used to evaluate time-course changes of the tumors. Results The average displacement of landmarks was 0.02±0.16 mm and 0.12±0.60 mm for proximal and distal landmarks after LDDMM transformation with cascading elasticity control, which was significantly smaller than 3.11±2.47 mm and 3.99±3.05 mm, respectively, after affine transformation. Emerged or vanished nodules were visualized on subtraction images, and enlarging or shrinking nodules were displayed on Jacobian maps enabled by highly accurate registration of the nodules using LDDMM. However, some residual misalignments were observed, even with non-linear transformation when substantial changes existed between the image pairs. Conclusions LDDMM provides accurate registration of serial lung CT images, and temporal subtraction images with Jacobian maps help radiologists to find changes in pulmonary nodules.
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Demir B, Bovolo F, Bruzzone L. Classification of time series of multispectral images with limited training data. IEEE TRANSACTIONS ON IMAGE PROCESSING : A PUBLICATION OF THE IEEE SIGNAL PROCESSING SOCIETY 2013; 22:3219-3233. [PMID: 23743777 DOI: 10.1109/tip.2013.2259838] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Image classification usually requires the availability of reliable reference data collected for the considered image to train supervised classifiers. Unfortunately when time series of images are considered, this is seldom possible because of the costs associated with reference data collection. In most of the applications it is realistic to have reference data available for one or few images of a time series acquired on the area of interest. In this paper, we present a novel system for automatically classifying image time series that takes advantage of image(s) with an associated reference information (i.e., the source domain) to classify image(s) for which reference information is not available (i.e., the target domain). The proposed system exploits the already available knowledge on the source domain and, when possible, integrates it with a minimum amount of new labeled data for the target domain. In addition, it is able to handle possible significant differences between statistical distributions of the source and target domains. Here, the method is presented in the context of classification of remote sensing image time series, where ground reference data collection is a highly critical and demanding task. Experimental results show the effectiveness of the proposed technique. The method can work on multimodal (e.g., multispectral) images.
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Affiliation(s)
- Begum Demir
- Department of Information Engineering and Computer Science, University of Trento, Trento I-38123, Italy.
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10
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Simões R, Slump C. Change detection and classification in brain MR images using change vector analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2012; 2011:7803-7. [PMID: 22256148 DOI: 10.1109/iembs.2011.6091923] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The automatic detection of longitudinal changes in brain images is valuable in the assessment of disease evolution and treatment efficacy. Most existing change detection methods that are currently used in clinical research to monitor patients suffering from neurodegenerative diseases--such as Alzheimer's--focus on large-scale brain deformations. However, such patients often have other brain impairments, such as infarcts, white matter lesions and hemorrhages, which are typically overlooked by the deformation-based methods. Other unsupervised change detection algorithms have been proposed to detect tissue intensity changes. The outcome of these methods is typically a binary change map, which identifies changed brain regions. However, understanding what types of changes these regions underwent is likely to provide equally important information about lesion evolution. In this paper, we present an unsupervised 3D change detection method based on Change Vector Analysis. We compute and automatically threshold the Generalized Likelihood Ratio map to obtain a binary change map. Subsequently, we perform histogram-based clustering to classify the change vectors. We obtain a Kappa Index of 0.82 using various types of simulated lesions. The classification error is 2%. Finally, we are able to detect and discriminate both small changes and ventricle expansions in datasets from Mild Cognitive Impairment patients.
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Affiliation(s)
- Rita Simões
- Signals and Systems Group, Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7500 AE Enschede, The Netherlands.
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11
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Automated detection of multiple sclerosis lesions in serial brain MRI. Neuroradiology 2011; 54:787-807. [DOI: 10.1007/s00234-011-0992-6] [Citation(s) in RCA: 60] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2011] [Accepted: 11/29/2011] [Indexed: 01/29/2023]
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12
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Godil SS, Shamim MS, Enam SA, Qidwai U. Fuzzy logic: A "simple" solution for complexities in neurosciences? Surg Neurol Int 2011; 2:24. [PMID: 21541006 PMCID: PMC3050069 DOI: 10.4103/2152-7806.77177] [Citation(s) in RCA: 35] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2010] [Accepted: 01/03/2011] [Indexed: 11/24/2022] Open
Abstract
Background: Fuzzy logic is a multi-valued logic which is similar to human thinking and interpretation. It has the potential of combining human heuristics into computer-assisted decision making, which is applicable to individual patients as it takes into account all the factors and complexities of individuals. Fuzzy logic has been applied in all disciplines of medicine in some form and recently its applicability in neurosciences has also gained momentum. Methods: This review focuses on the use of this concept in various branches of neurosciences including basic neuroscience, neurology, neurosurgery, psychiatry and psychology. Results: The applicability of fuzzy logic is not limited to research related to neuroanatomy, imaging nerve fibers and understanding neurophysiology, but it is also a sensitive and specific tool for interpretation of EEGs, EMGs and MRIs and an effective controller device in intensive care units. It has been used for risk stratification of stroke, diagnosis of different psychiatric illnesses and even planning neurosurgical procedures. Conclusions: In the future, fuzzy logic has the potential of becoming the basis of all clinical decision making and our understanding of neurosciences.
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Affiliation(s)
- Saniya Siraj Godil
- Faculty of Health Sciences, Medical College, Aga Khan University, Karachi, Pakistan
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A method to analyze the evolution of malignant gliomas using MRI. Int J Comput Assist Radiol Surg 2008. [DOI: 10.1007/s11548-008-0263-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Patriarche JW, Erickson BJ. Part 1. Automated change detection and characterization in serial MR studies of brain-tumor patients. J Digit Imaging 2007; 20:203-22. [PMID: 17216385 PMCID: PMC3043896 DOI: 10.1007/s10278-006-1038-1] [Citation(s) in RCA: 14] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
The goal of this study was to create an algorithm which would quantitatively compare serial magnetic resonance imaging studies of brain-tumor patients. A novel algorithm and a standard classify-subtract algorithm were constructed. The ability of both algorithms to detect and characterize changes was compared using a series of digital phantoms. The novel algorithm achieved a mean sensitivity of 0.87 (compared with 0.59 for classify-subtract) and a mean specificity of 0.98 (compared with 0.92 for classify-subtract) with regard to identification of voxels as changing or unchanging and classification of voxels into types of change. The novel algorithm achieved perfect specificity in seven of the nine experiments. The novel algorithm was additionally applied to a short series of clinical cases, where it was shown to identify visually subtle changes. Automated change detection and characterization could facilitate objective review and understanding of serial magnetic resonance imaging studies in brain-tumor patients.
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15
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Khorasani R, Erickson BJ, Patriarche J. New opportunities in computer-aided diagnosis: change detection and characterization. J Am Coll Radiol 2007; 3:468-9. [PMID: 17412102 DOI: 10.1016/j.jacr.2006.03.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2006] [Indexed: 10/24/2022]
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Erickson BJ, Patriarche J, Wood C, Campeau N, Lindell EP, Savcenko V, Arslanlar N, Wang L. Image registration improves confidence and accuracy of image interpretation. Cancer Inform 2007; 4:19-24. [PMID: 19390661 PMCID: PMC2666945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
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Patriarche JW, Erickson BJ. Change detection & characterization: a new tool for imaging informatics and cancer research. Cancer Inform 2007; 4:1-11. [PMID: 19390659 PMCID: PMC2666947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Modern imaging systems are able to produce a rich and diverse array of information, regarding various facets of anatomy and function. The quantity of information produced by these systems is so bountiful, however, as to have the potential to become a hindrance to clinical assessment. In the context of serial image evaluation, computer-based change detection and characterization is one important mechanism to process the information produced by imaging systems, so as to reduce the quantity of data, direct the attention of the physician to regions of the data which are the most informative for their purposes, and present the data in the form in which it will be the most useful. Change detection and characterization algorithms may serve as a basis for the creation of an objective definition of progression, which will reduce inter and intra-observer variability, and facilitate earlier detection of disease and recurrence, which in turn may lead to improved outcomes. Decreased observer variability combined with increased acuity should make it easier to discover promising therapies. Quantitative measures of the response to these therapies should provide a means to compare the effectiveness of treatments under investigation. Change detection may be applicable to a broad range of cancers, in essentially all anatomical regions. The source of information upon which change detection comparisons may be based is likewise broad. Validation of algorithms for the longitudinal assessment of cancer patients is expected to be challenging, though not insurmountable, as the many facets of the problem mean that validation will likely need to be approached from a variety of vantage points. Change detection and characterization is quickly becoming a very active field of investigation, and it is expected that this burgeoning field will help to facilitate cancer care both in the clinic and research.
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Erickson BJ, Patriarche J, Wood C, Campeau N, Lindell EP, Savcenko V, Arslanlar N, Wang L. Image Registration Improves Confidence and Accuracy of Image Interpretation. Cancer Inform 2007. [DOI: 10.1177/117693510700400004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Affiliation(s)
| | | | | | | | | | | | | | - Liqin Wang
- Department of Radiology, Mayo Clinic, Rochester, MN
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Patriarche JW, Erickson BJ. Change Detection & Characterization: A New Tool for Imaging Informatics and Cancer Research. Cancer Inform 2007. [DOI: 10.1177/117693510700400002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Modern imaging systems are able to produce a rich and diverse array of information, regarding various facets of anatomy and function. The quantity of information produced by these systems is so bountiful, however, as to have the potential to become a hindrance to clinical assessment. In the context of serial image evaluation, computer-based change detection and characterization is one important mechanism to process the information produced by imaging systems, so as to reduce the quantity of data, direct the attention of the physician to regions of the data which are the most informative for their purposes, and present the data in the form in which it will be the most useful. Change detection and characterization algorithms may serve as a basis for the creation of an objective definition of progression, which will reduce inter and intra-observer variability, and facilitate earlier detection of disease and recurrence, which in turn may lead to improved outcomes. Decreased observer variability combined with increased acuity should make it easier to discover promising therapies. Quantitative measures of the response to these therapies should provide a means to compare the effectiveness of treatments under investigation. Change detection may be applicable to a broad range of cancers, in essentially all anatomical regions. The source of information upon which change detection comparisons may be based is likewise broad. Validation of algorithms for the longitudinal assessment of cancer patients is expected to be challenging, though not insurmountable, as the many facets of the problem mean that validation will likely need to be approached from a variety of vantage points. Change detection and characterization is quickly becoming a very active field of investigation, and it is expected that this burgeoning field will help to facilitate cancer care both in the clinic and research.
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