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A Survey on Computer-Aided Diagnosis of Brain Disorders through MRI Based on Machine Learning and Data Mining Methodologies with an Emphasis on Alzheimer Disease Diagnosis and the Contribution of the Multimodal Fusion. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10051894] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Computer-aided diagnostic (CAD) systems use machine learning methods that provide a synergistic effect between the neuroradiologist and the computer, enabling an efficient and rapid diagnosis of the patient’s condition. As part of the early diagnosis of Alzheimer’s disease (AD), which is a major public health problem, the CAD system provides a neuropsychological assessment that helps mitigate its effects. The use of data fusion techniques by CAD systems has proven to be useful, they allow for the merging of information relating to the brain and its tissues from MRI, with that of other types of modalities. This multimodal fusion refines the quality of brain images by reducing redundancy and randomness, which contributes to improving the clinical reliability of the diagnosis compared to the use of a single modality. The purpose of this article is first to determine the main steps of the CAD system for brain magnetic resonance imaging (MRI). Then to bring together some research work related to the diagnosis of brain disorders, emphasizing AD. Thus the most used methods in the stages of classification and brain regions segmentation are described, highlighting their advantages and disadvantages. Secondly, on the basis of the raised problem, we propose a solution within the framework of multimodal fusion. In this context, based on quantitative measurement parameters, a performance study of multimodal CAD systems is proposed by comparing their effectiveness with those exploiting a single MRI modality. In this case, advances in information fusion techniques in medical imagery are accentuated, highlighting their advantages and disadvantages. The contribution of multimodal fusion and the interest of hybrid models are finally addressed, as well as the main scientific assertions made, in the field of brain disease diagnosis.
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Cover GS, Herrera WG, Bento MP, Appenzeller S, Rittner L. Computational methods for corpus callosum segmentation on MRI: A systematic literature review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 154:25-35. [PMID: 29249344 DOI: 10.1016/j.cmpb.2017.10.025] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/25/2016] [Revised: 10/23/2017] [Accepted: 10/30/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE The corpus callosum (CC) is the largest white matter structure in the brain and has a significant role in central nervous system diseases. Its volume correlates with the severity and/or extent of neurodegenerative disease. Even though the CC's role has been extensively studied over the last decades, and different algorithms and methods have been published regarding CC segmentation and parcellation, no reviews or surveys covering such developments have been reported so far. To bridge this gap, this paper presents a systematic literature review of computational methods focusing on CC segmentation and parcellation acquired on magnetic resonance imaging. METHODS IEEExplore, PubMed, EBSCO Host, and Scopus database were searched with the following search terms: ((Segmentation OR Parcellation) AND (Corpus Callosum) AND (DTI OR MRI OR Diffusion Tensor Imag* OR Diffusion Tractography OR Magnetic Resonance Imag*)), resulting in 802 publications. Two reviewers independently evaluated all articles and 36 studies were selected through the systematic literature review process. RESULTS This work reviewed four main segmentation methods groups: model-based, region-based, thresholding, and machine learning; 32 different validity metrics were reported. Even though model-based techniques are the most recurrently used for the segmentation task (13 articles), machine learning approaches achieved better outcomes of 95% when analyzing mean values for segmentation and classification metrics results. Moreover, CC segmentation is better established in T1-weighted images, having more methods implemented and also being tested in larger datasets, compared with diffusion tensor images. CONCLUSIONS The analyzed computational methods used to perform CC segmentation on magnetic resonance imaging have not yet overcome all presented challenges owing to metrics variability and lack of traceable materials.
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Affiliation(s)
- G S Cover
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil.
| | - W G Herrera
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - M P Bento
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
| | - S Appenzeller
- Rheumatology Division, Faculty of Medical Science, University of Campinas, Brazil
| | - L Rittner
- MICLab - Medical Image Computing Laboratory, School of Electrical and Computer Engineering, University of Campinas, Brazil
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Liu H, Geng F, Guo Q, Zhang C, Zhang C. A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm. Soft comput 2017. [DOI: 10.1007/s00500-017-2608-5] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Mesejo P, Ibáñez Ó, Cordón Ó, Cagnoni S. A survey on image segmentation using metaheuristic-based deformable models: state of the art and critical analysis. Appl Soft Comput 2016. [DOI: 10.1016/j.asoc.2016.03.004] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Zhao F, Xie X. Energy minimization in medical image analysis: Methodologies and applications. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2016; 32:e02733. [PMID: 26186171 DOI: 10.1002/cnm.2733] [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: 05/05/2015] [Revised: 06/23/2015] [Accepted: 06/23/2015] [Indexed: 06/04/2023]
Abstract
Energy minimization is of particular interest in medical image analysis. In the past two decades, a variety of optimization schemes have been developed. In this paper, we present a comprehensive survey of the state-of-the-art optimization approaches. These algorithms are mainly classified into two categories: continuous method and discrete method. The former includes Newton-Raphson method, gradient descent method, conjugate gradient method, proximal gradient method, coordinate descent method, and genetic algorithm-based method, while the latter covers graph cuts method, belief propagation method, tree-reweighted message passing method, linear programming method, maximum margin learning method, simulated annealing method, and iterated conditional modes method. We also discuss the minimal surface method, primal-dual method, and the multi-objective optimization method. In addition, we review several comparative studies that evaluate the performance of different minimization techniques in terms of accuracy, efficiency, or complexity. These optimization techniques are widely used in many medical applications, for example, image segmentation, registration, reconstruction, motion tracking, and compressed sensing. We thus give an overview on those applications as well.
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Affiliation(s)
- Feng Zhao
- Department of Computer Science, Swansea University, Swansea, SA2 8PP, UK
| | - Xianghua Xie
- Department of Computer Science, Swansea University, Swansea, SA2 8PP, UK
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Corpus Callosum Segmentation in MS Studies Using Normal Atlases and Optimal Hybridization of Extrinsic and Intrinsic Image Cues. ACTA ACUST UNITED AC 2015. [DOI: 10.1007/978-3-319-24574-4_15] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
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Dolz J, Massoptier L, Vermandel M. Segmentation algorithms of subcortical brain structures on MRI for radiotherapy and radiosurgery: A survey. Ing Rech Biomed 2015. [DOI: 10.1016/j.irbm.2015.06.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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Mesejo P, Valsecchi A, Marrakchi-Kacem L, Cagnoni S, Damas S. Biomedical image segmentation using geometric deformable models and metaheuristics. Comput Med Imaging Graph 2015; 43:167-78. [DOI: 10.1016/j.compmedimag.2013.12.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2013] [Revised: 06/14/2013] [Accepted: 12/11/2013] [Indexed: 10/25/2022]
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Research on a pulmonary nodule segmentation method combining fast self-adaptive FCM and classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2015; 2015:185726. [PMID: 25945120 PMCID: PMC4405023 DOI: 10.1155/2015/185726] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/09/2014] [Revised: 11/01/2014] [Accepted: 12/21/2014] [Indexed: 12/02/2022]
Abstract
The key problem of computer-aided diagnosis (CAD) of lung cancer is to segment pathologically changed tissues fast and accurately. As pulmonary nodules are potential manifestation of lung cancer, we propose a fast and self-adaptive pulmonary nodules segmentation method based on a combination of FCM clustering and classification learning. The enhanced spatial function considers contributions to fuzzy membership from both the grayscale similarity between central pixels and single neighboring pixels and the spatial similarity between central pixels and neighborhood and improves effectively the convergence rate and self-adaptivity of the algorithm. Experimental results show that the proposed method can achieve more accurate segmentation of vascular adhesion, pleural adhesion, and ground glass opacity (GGO) pulmonary nodules than other typical algorithms.
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Nosrati MS, Hamarneh G. Local optimization based segmentation of spatially-recurring, multi-region objects with part configuration constraints. IEEE TRANSACTIONS ON MEDICAL IMAGING 2014; 33:1845-1859. [PMID: 24835214 DOI: 10.1109/tmi.2014.2323074] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Incorporating prior knowledge into image segmentation algorithms has proven useful for obtaining more accurate and plausible results. Two important constraints, containment and exclusion of regions, have gained attention in recent years mainly due to their descriptive power. In this paper, we augment the level set framework with the ability to handle these two intuitive geometric relationships, containment and exclusion, along with a distance constraint between boundaries of multi-region objects. Level set's important property of automatically handling topological changes of evolving contours/surfaces enables us to segment spatially-recurring objects (e.g., multiple instances of multi-region cells in a large microscopy image) while satisfying the two aforementioned constraints. In addition, the level set approach gives us a very simple and natural way to compute the distance between contours/surfaces and impose constraints on it. The downside, however, is a local optimization framework in which the final segmentation solution depends on the initialization. In fact, here, we sacrifice the optimizability (local instead of global solution) in exchange for lower space complexity (less memory usage) and faster runtime (especially for large microscopic images) as well as no grid artifacts. Nevertheless, the result from validating our method on several biomedical applications showed the utility and advantages of this augmented level set framework (even with rough initialization that is distant from the desired boundaries). We also compared our framework with its counterpart methods in the discrete domain and reported the pros and cons of each of these methods in terms of metrication error and efficiency in memory usage and runtime.
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Taherdangkoo M, Bagheri MH, Yazdi M, Andriole KP. An effective method for segmentation of MR brain images using the ant colony optimization algorithm. J Digit Imaging 2014; 26:1116-23. [PMID: 23563793 DOI: 10.1007/s10278-013-9596-5] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
Abstract
Since segmentation of magnetic resonance images is one of the most important initial steps in brain magnetic resonance image processing, success in this part has a great influence on the quality of outcomes of subsequent steps. In the past few decades, numerous methods have been introduced for classification of such images, but typically they perform well only on a specific subset of images, do not generalize well to other image sets, and have poor computational performance. In this study, we provided a method for segmentation of magnetic resonance images of the brain that despite its simplicity has a high accuracy. We compare the performance of our proposed algorithm with similar evolutionary algorithms on a pixel-by-pixel basis. Our algorithm is tested across varying sets of magnetic resonance images and demonstrates high speed and accuracy. It should be noted that in initial steps, the algorithm is computationally intensive requiring a large number of calculations; however, in subsequent steps of the search process, the number is reduced with the segmentation focused only in the target area.
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Automatic hippocampus localization in histological images using Differential Evolution-based deformable models. Pattern Recognit Lett 2013. [DOI: 10.1016/j.patrec.2012.10.012] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Ulén J, Strandmark P, Kahl F. An efficient optimization framework for multi-region segmentation based on Lagrangian duality. IEEE TRANSACTIONS ON MEDICAL IMAGING 2013; 32:178-188. [PMID: 22987510 DOI: 10.1109/tmi.2012.2218117] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
We introduce a multi-region model for simultaneous segmentation of medical images. In contrast to many other models, geometric constraints such as inclusion and exclusion between the regions are enforced, which makes it possible to correctly segment different regions even if the intensity distributions are identical. We efficiently optimize the model using a combination of graph cuts and Lagrangian duality which is faster and more memory efficient than current state of the art. As the method is based on global optimization techniques, the resulting segmentations are independent of initialization. We apply our framework to the segmentation of the left and right ventricles, myocardium and the left ventricular papillary muscles in magnetic resonance imaging and to lung segmentation in full-body X-ray computed tomography. We evaluate our approach on a publicly available benchmark with competitive results.
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Affiliation(s)
- Johannes Ulén
- Centre for Mathematical Sciences, Lund University, Lund, Sweden.
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Bova N, Ibanez O, Cordon O. Image Segmentation Using Extended Topological Active Nets Optimized by Scatter Search. IEEE COMPUT INTELL M 2013. [DOI: 10.1109/mci.2012.2228587] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Neubert A, Fripp J, Engstrom C, Schwarz R, Lauer L, Salvado O, Crozier S. Automated detection, 3D segmentation and analysis of high resolution spine MR images using statistical shape models. Phys Med Biol 2012. [PMID: 23201861 DOI: 10.1088/0031-9155/57/24/8357] [Citation(s) in RCA: 79] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Recent advances in high resolution magnetic resonance (MR) imaging of the spine provide a basis for the automated assessment of intervertebral disc (IVD) and vertebral body (VB) anatomy. High resolution three-dimensional (3D) morphological information contained in these images may be useful for early detection and monitoring of common spine disorders, such as disc degeneration. This work proposes an automated approach to extract the 3D segmentations of lumbar and thoracic IVDs and VBs from MR images using statistical shape analysis and registration of grey level intensity profiles. The algorithm was validated on a dataset of volumetric scans of the thoracolumbar spine of asymptomatic volunteers obtained on a 3T scanner using the relatively new 3D T2-weighted SPACE pulse sequence. Manual segmentations and expert radiological findings of early signs of disc degeneration were used in the validation. There was good agreement between manual and automated segmentation of the IVD and VB volumes with the mean Dice scores of 0.89 ± 0.04 and 0.91 ± 0.02 and mean absolute surface distances of 0.55 ± 0.18 mm and 0.67 ± 0.17 mm respectively. The method compares favourably to existing 3D MR segmentation techniques for VBs. This is the first time IVDs have been automatically segmented from 3D volumetric scans and shape parameters obtained were used in preliminary analyses to accurately classify (100% sensitivity, 98.3% specificity) disc abnormalities associated with early degenerative changes.
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Affiliation(s)
- A Neubert
- The Australian E-Health Research Centre, CSIRO ICT Centre, Brisbane, Australia.
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