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Brain Tumour Classification Using Noble Deep Learning Approach with Parametric Optimization through Metaheuristics Approaches. COMPUTERS 2022. [DOI: 10.3390/computers11010010] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Deep learning has surged in popularity in recent years, notably in the domains of medical image processing, medical image analysis, and bioinformatics. In this study, we offer a completely autonomous brain tumour segmentation approach based on deep neural networks (DNNs). We describe a unique CNN architecture which varies from those usually used in computer vision. The classification of tumour cells is very difficult due to their heterogeneous nature. From a visual learning and brain tumour recognition point of view, a convolutional neural network (CNN) is the most extensively used machine learning algorithm. This paper presents a CNN model along with parametric optimization approaches for analysing brain tumour magnetic resonance images. The accuracy percentage in the simulation of the above-mentioned model is exactly 100% throughout the nine runs, i.e., Taguchi’s L9 design of experiment. This comparative analysis of all three algorithms will pique the interest of readers who are interested in applying these techniques to a variety of technical and medical challenges. In this work, the authors have tuned the parameters of the convolutional neural network approach, which is applied to the dataset of Brain MRIs to detect any portion of a tumour, through new advanced optimization techniques, i.e., SFOA, FBIA and MGA.
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Wen Q, Yang H, Li J, Zhang J, Tong H, Ye Q, Zhong K. Ultra-High-Resolution in vitro MRI Study of Age-Related Brain Subcortical Susceptibility Alteration in Rhesus Monkeys at 9.4 T. Front Aging Neurosci 2020; 12:259. [PMID: 33013351 PMCID: PMC7461968 DOI: 10.3389/fnagi.2020.00259] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2020] [Accepted: 07/27/2020] [Indexed: 11/29/2022] Open
Abstract
Iron concentration in the brain has been suggested as a biomarker of pathologic neurodegeneration. However, the iron concentration changes in healthy aging as well. This study aimed to quantify the age-related changes in iron concentration in the gray matter of healthy rhesus monkeys using quantitative susceptibility mapping (QSM). Three-dimensional gradient-echo images of 16 female rhesus monkey brains aged between 2 and 26 years were acquired in vitro. The susceptibilities in the brain regions of the caudate nucleus (Cd), putamen (Pt), globus pallidus (Gp), and substantia nigra (Sn) were analyzed. The susceptibility varied across different brain regions, with higher levels in the Gp and Sn. Susceptibilities in all analyzed brain regions were linearly correlated with age, yet the plateau period as observed in human brains was absent. This is the first in vitro report of the age-related variability of susceptibility in the deep gray matter of rhesus monkey brains at 9.4 T, with an isotropic resolution of 150 μm. Awareness of age-related changes in susceptibility is vital for the establishment of a baseline to facilitate the differentiation of pathologic neurodegeneration from healthy aging in non-human primate studies.
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Affiliation(s)
- Qingqing Wen
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Hongyi Yang
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,University of Science and Technology of China, Hefei, China
| | - Jiali Li
- Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China
| | - Jin Zhang
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Haiyang Tong
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Qiong Ye
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China
| | - Kai Zhong
- High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.,University of Science and Technology of China, Hefei, China.,Key Laboratory of Anhui Province for High Field Magnetic Resonance Imaging, Hefei, China.,Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
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Tohka J. Partial volume effect modeling for segmentation and tissue classification of brain magnetic resonance images: A review. World J Radiol 2014; 6:855-864. [PMID: 25431640 PMCID: PMC4241492 DOI: 10.4329/wjr.v6.i11.855] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2014] [Revised: 09/03/2014] [Accepted: 09/24/2014] [Indexed: 02/06/2023] Open
Abstract
Quantitative analysis of magnetic resonance (MR) brain images are facilitated by the development of automated segmentation algorithms. A single image voxel may contain of several types of tissues due to the finite spatial resolution of the imaging device. This phenomenon, termed partial volume effect (PVE), complicates the segmentation process, and, due to the complexity of human brain anatomy, the PVE is an important factor for accurate brain structure quantification. Partial volume estimation refers to a generalized segmentation task where the amount of each tissue type within each voxel is solved. This review aims to provide a systematic, tutorial-like overview and categorization of methods for partial volume estimation in brain MRI. The review concentrates on the statistically based approaches for partial volume estimation and also explains differences to other, similar image segmentation approaches.
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Coupé P, Manjón JV, Fonov V, Pruessner J, Robles M, Collins DL. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 2010; 54:940-54. [PMID: 20851199 DOI: 10.1016/j.neuroimage.2010.09.018] [Citation(s) in RCA: 408] [Impact Index Per Article: 29.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2010] [Revised: 09/03/2010] [Accepted: 09/08/2010] [Indexed: 10/19/2022] Open
Abstract
Quantitative magnetic resonance analysis often requires accurate, robust, and reliable automatic extraction of anatomical structures. Recently, template-warping methods incorporating a label fusion strategy have demonstrated high accuracy in segmenting cerebral structures. In this study, we propose a novel patch-based method using expert manual segmentations as priors to achieve this task. Inspired by recent work in image denoising, the proposed nonlocal patch-based label fusion produces accurate and robust segmentation. Validation with two different datasets is presented. In our experiments, the hippocampi of 80 healthy subjects and the lateral ventricles of 80 patients with Alzheimer's disease were segmented. The influence on segmentation accuracy of different parameters such as patch size and number of training subjects was also studied. A comparison with an appearance-based method and a template-based method was also carried out. The highest median kappa index values obtained with the proposed method were 0.884 for hippocampus segmentation and 0.959 for lateral ventricle segmentation.
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Affiliation(s)
- Pierrick Coupé
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada.
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Lee JD, Su HR, Cheng PE, Liou M, Aston JAD, Tsai AC, Chen CY. MR image segmentation using a power transformation approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2009; 28:894-905. [PMID: 19164075 DOI: 10.1109/tmi.2009.2012896] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
Abstract
This study proposes a segmentation method for brain MR images using a distribution transformation approach. The method extends traditional Gaussian mixtures expectation-maximization segmentation to a power transformed version of mixed intensity distributions, which includes Gaussian mixtures as a special case. As MR intensities tend to exhibit non-Gaussianity due to partial volume effects, the proposed method is designed to fit non-Gaussian tissue intensity distributions. One advantage of the method is that it is intuitively appealing and computationally simple. To avoid performance degradation caused by intensity inhomogeneity, different methods for correcting bias fields were applied prior to image segmentation, and their correction effects on the segmentation results were examined in the empirical study. The partitions of brain tissues (i.e., gray and white matter) resulting from the method were validated and evaluated against manual segmentation results based on 38 real T1-weighted image volumes from the internet brain segmentation repository, and 18 simulated image volumes from BrainWeb. The Jaccard and Dice similarity indexes were computed to evaluate the performance of the proposed approach relative to the expert segmentations. Empirical results suggested that the proposed segmentation method yielded higher similarity measures for both gray matter and white matter as compared with those based on the traditional segmentation using the Gaussian mixtures approach.
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Affiliation(s)
- Juin-Der Lee
- Institute of Statistical Science, Academia Sinica, Taipei, Taiwan
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Manjón JV, Tohka J, García-Martí G, Carbonell-Caballero J, Lull JJ, Martí-Bonmatí L, Robles M. Robust MRI brain tissue parameter estimation by multistage outlier rejection. Magn Reson Med 2008; 59:866-73. [PMID: 18383286 DOI: 10.1002/mrm.21521] [Citation(s) in RCA: 49] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
This article addresses the problem of the tissue type parameter estimation in brain MRI in the presence of partial volume effects. Automatic MRI brain tissue classification is hampered by partial volume effects that are caused by the finite resolution of the acquisition process. Due to this effect intensity distributions in brain MRI cannot be well modeled by a simple mixture of Gaussians and therefore more complex models have been developed. Unfortunately, these models do not seem to be robust enough for clinical conditions, as the quality of the tissue classification decreases rapidly with the image quality. Also, the application of these methods for pathological images with unmodeled intensities (e.g. MS plaques, tumors, etc.) remains uncertain. In the present work a new robust method for brain tissue characterization is presented, treating the partial volume affected voxels as outliers of the pure tissue distributions. The proposed method estimates the tissue characteristics from a reduced set of intensities belonging to a particular pure tissue class. This reduced set is selected by using a trimming procedure based on local gradient information and distributional data. This feature makes the method highly tolerant of a large amount of unexpected intensities without degrading its performance. The proposed method has been evaluated using both synthetic and real MR data and compared with state-of-the-art methods showing the best results in the comparative.
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Affiliation(s)
- José V Manjón
- IBIME Group, ITACA Institute, Polytechnic University of Valencia, Valencia, Spain.
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Montagner J, Barra V, Boire JY. Synthesis of a functional information with anatomical landmarks by multiresolution fusion of brain images. CONFERENCE PROCEEDINGS : ... ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL CONFERENCE 2007; 2005:6547-50. [PMID: 17281770 DOI: 10.1109/iembs.2005.1616000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
In order to help clinicians with the diagnosis of neurodegenerative diseases, we provide a synthetic functional information located in relation with anatomical structures. The final image is processed by multimodal data fusion between SPECT and MR images. We propose a new method for the management of such multiresolution data, in which a geometrical model allows an accurate correspondence of voxels from both images, while preserving at best both original pieces of information. We use this matching method to replace the interpolation step in the compulsory image registration of the data fusion process. The geometrical model is first built from registration parameters. Computational geometry algorithms, applied to this model, allow the computation of numerical values used to process the final information. The method has been applied to brain perfusion and neurotransmission SPECT images.
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Affiliation(s)
- J Montagner
- ERIM, Faculty of Medicine, BP 38, 63001 Clermont-Ferrand Cedex 1, France
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Park HJ, Lee JD, Chun JW, Seok JH, Yun M, Oh MK, Kim JJ. Cortical surface-based analysis of 18F-FDG PET: measured metabolic abnormalities in schizophrenia are affected by cortical structural abnormalities. Neuroimage 2006; 31:1434-44. [PMID: 16540349 DOI: 10.1016/j.neuroimage.2006.02.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2005] [Revised: 01/31/2006] [Accepted: 02/01/2006] [Indexed: 11/25/2022] Open
Abstract
The purpose of the study is to propose a new framework for surface-based statistical parametric mapping of PET images using MRI-based cortical surface analysis, including partial volume correction, intensity normalization and spatial normalization on the cortical surface. Maximum PET intensities along the path between inner and outer layer of the cortical gray matter are mapped onto the cortical surface to generate a metabolic activity surface map. For the partial volume correction, the metabolic activity surface map was divided by the partial volume effect map. The regional metabolic activity was normalized by the global activity iteratively calculated at the surface nodes, statistically independent of the group, as measured by F statistics. After surface-based spatial normalization, a statistical evaluation of both cortical thickness and cortical metabolic activity was conducted on the normalized surfaces of 16 patients with schizophrenia and 16 age- and gender-matched healthy controls. The patients with schizophrenia were found to have significant cortical thinning in the temporal and inferior frontal cortices. Accordingly, their PET imaging was significantly affected by the partial volume effect, indicating that partial volume correction could change the statistical results. After correction of the partial volume effects, the patients showed hyperactivity in the temporal cortex, whereas hypoactivity in the prefrontal cortex, predominantly in the left hemisphere. Our results demonstrate that anatomical factors affect an analysis for functional data from the PET, and therefore the importance of combining anatomy and function in the analysis of imaging data for schizophrenia should be considered.
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Affiliation(s)
- Hae-Jeong Park
- Department of Diagnostic Radiology, Division of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea
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Monziols M, Collewet G, Bonneau M, Mariette F, Davenel A, Kouba M. Quantification of muscle, subcutaneous fat and intermuscular fat in pig carcasses and cuts by magnetic resonance imaging. Meat Sci 2006; 72:146-54. [DOI: 10.1016/j.meatsci.2005.06.018] [Citation(s) in RCA: 46] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2005] [Revised: 06/24/2005] [Accepted: 06/24/2005] [Indexed: 11/25/2022]
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Heverhagen JT, Boehm D, Klose KJ. Calibrated magnetic resonance hydrometry: an in vitro study. J Magn Reson Imaging 2003; 17:472-7. [PMID: 12655587 DOI: 10.1002/jmri.10267] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
PURPOSE To demonstrate a quantitative approach to measuring fluid volumes with standard single shot RARE sequences. MATERIALS AND METHODS In phantom experiments, magnetic resonance hydrometry (MRH), in combination with various calibration phantoms (5 mL up to 500 mL) as internal standards, was used to quantify fluid volumes. In total, 16 volume phantoms were investigated with six different calibration phantoms. In addition, noise correction was implemented to correct the quantification results and to avoid the influence of random noise in the image. RESULTS All MR measurements show significant correlations of up to r = 0.99 (P <.05) with the real applied volume in the investigated phantoms. However, measurements of large volumes were more precise with large calibration phantoms. Noise reduction did not change the correlation between measured and real applied volumes, but did reduce the error of the measured volumes. Calibrated magnetic resonance hydrometry (cMRH) is able to quantify volumes of fluid fast and noninvasively. The volumes of the used calibration phantoms have to be at least in the order of magnitude of the volumes that are to be measured. CONCLUSION In vitro, cMRH using a single-shot rapid acquisition with refocused echoes (ssRARE) sequence and calibration phantoms is a fast and accurate method of quantifying steady amounts of fluid.
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Affiliation(s)
- Johannes T Heverhagen
- Department of Diagnostic Radiology, University Hospital, Philipps University, Marburg, Germany.
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Wang D, Chalk JB, Rose SE, de Zubicaray G, Cowin G, Galloway GJ, Barnes D, Spooner D, Doddrell DM, Semple J. MR image-based measurement of rates of change in volumes of brain structures. Part II: application to a study of Alzheimer's disease and normal aging. Magn Reson Imaging 2002; 20:41-8. [PMID: 11973028 DOI: 10.1016/s0730-725x(02)00472-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
We present global and regional rates of brain atrophy measured on serially acquired T1-weighted brain MR images for a group of Alzheimer's disease (AD) patients and age-matched normal control (NC) subjects using the analysis procedure described in Part I. Three rates of brain atrophy: the rate of atrophy in the cerebrum, the rate of lateral ventricular enlargement and the rate of atrophy in the region of temporal lobes, were evaluated for 14 AD patients and 14 age-matched NC subjects. All three rates showed significant differences between the two groups. However, the greatest separation of the two groups was obtained when the regional rates were combined. This application has demonstrated that rates of brain atrophy, especially in specific regions of the brain, based on MR images can provide sensitive measures for evaluating the progression of AD. These measures will be useful for the evaluation of therapeutic effects of novel therapies for AD.
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Affiliation(s)
- Deming Wang
- Centre for Magnetic Resonance, The University of Queensland, St. Lucia, Brisbane 4072, Australia.
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Barra V, Frenoux E, Boire JY. Automatic volumetric measurement of lateral ventricles on magnetic resonance images with correction of partial volume effects. J Magn Reson Imaging 2002; 15:16-22. [PMID: 11793452 DOI: 10.1002/jmri.10032] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To propose a method for the quantification of lateral ventricle (LV) volumes on a single sequence of 3D magnetic resonance (MR) images. MATERIALS AND METHODS This algorithm, following a preliminary fuzzy tissue classification step, is based on the development of mathematical morphology processes allowing both the extraction of the LVs and the correction of partial volume effects on their boundaries. The procedure is fast and totally unsupervised. The method is tested on a phantom image, then applied to five patients diagnosed as potentially suffering from Alzheimer's disease, and finally applied on several MR acquisitions to show the genericness of the algorithm. RESULTS AND CONCLUSION This technique yielded both an accurate estimation of ventricular volumes intra- and intersubject with respect to published data and a relevant management of partial volume effects. Numerous clinical applications are now expected, from the study of schizophrenia to the longitudinal follow-up of Alzheimer's patients.
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