Magnetic resonance imaging bias field estimation and tissue segmentation via convolutional neural networks and multiplicative and additive intrinsic components optimization
Magnetic resonance imaging (MRI) brain tissue segmentation is essential for the diagnosis and treatment of neurological diseases; however, intensity inhomogeneity poses a significant challenge, particularly in data-limited scenarios. This study aimed to explore an algorithm designed to robustly addr...
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2025
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| Abstract | Magnetic resonance imaging (MRI) brain tissue segmentation is essential for the diagnosis and treatment of neurological diseases; however, intensity inhomogeneity poses a significant challenge, particularly in data-limited scenarios. This study aimed to explore an algorithm designed to robustly address intensity inhomogeneity for brain MRI tissue segmentation under dataset constraints.
We propose a two-stage framework that leverages data-driven and knowledge-driven approaches. Initially, a data-driven model was employed for skull-stripping, where a lightweight module, named multi-view dilated convolution attention (MDCA), is integrated into skip connections. The MDCA module eliminates the effect of intensity inhomogeneity enriched in shallow features at multiple scales, thus avoiding the negative impact on deeper abstract features. Furthermore, we introduced multiplicative and additive intrinsic components optimization (MAICO) algorithm, which decomposes MRI images into their real anatomical structures, multiplicative and additive bias fields, and zero-mean Gaussian noise, thus enabling precise anatomical segmentation. Experiments on MRBrainS13 and MRBrainS18 public datasets involved the random introduction of intensity inhomogeneity to generate training, validation, and testing sets with 60%, 20%, and 20% splits, respectively. Segmentation performance was measured using Dice coefficients and compared to methods such as MICO, FSL, and UNet. An ablation study further validated the efficacy of the MDCA module.
Our approach improved MRI brain tissue segmentation accuracy, achieving a mean Dice coefficient of 0.7733 across tissue types. With MDCA and MAICO, it reached 0.8163 for white matter, 0.7402 for gray matter, and 0.7634 for cerebrospinal fluid, outperforming other algorithms. Additionally, MDCA module integration in skip connections yielded a 5% average accuracy boost.
This study effectively combined knowledge-driven and data-driven techniques to enhance MRI brain segmentation stability and accuracy, thereby demonstrating strong potential for clinical application in managing intensity inhomogeneity in data-constrained settings. |
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| AbstractList | AbstractObjectiveMagnetic resonance imaging (MRI) brain tissue segmentation is essential for the diagnosis and treatment of neurological diseases; however, intensity inhomogeneity poses a significant challenge, particularly in data-limited scenarios. This study aimed to explore an algorithm designed to robustly address intensity inhomogeneity for brain MRI tissue segmentation under dataset constraints. MethodsWe propose a two-stage framework that leverages data-driven and knowledge-driven approaches. Initially, a data-driven model was employed for skull-stripping, where a lightweight module, named multi-view dilated convolution attention (MDCA), is integrated into skip connections. The MDCA module eliminates the effect of intensity inhomogeneity enriched in shallow features at multiple scales, thus avoiding the negative impact on deeper abstract features. Furthermore, we introduced multiplicative and additive intrinsic components optimization (MAICO) algorithm, which decomposes MRI images into their real anatomical structures, multiplicative and additive bias fields, and zero-mean Gaussian noise, thus enabling precise anatomical segmentation. Experiments on MRBrainS13 and MRBrainS18 public datasets involved the random introduction of intensity inhomogeneity to generate training, validation, and testing sets with 60%, 20%, and 20% splits, respectively. Segmentation performance was measured using Dice coefficients and compared to methods such as MICO, FSL, and UNet. An ablation study further validated the efficacy of the MDCA module. ResultsOur approach improved MRI brain tissue segmentation accuracy, achieving a mean Dice coefficient of 0.7733 across tissue types. With MDCA and MAICO, it reached 0.8163 for white matter, 0.7402 for gray matter, and 0.7634 for cerebrospinal fluid, outperforming other algorithms. Additionally, MDCA module integration in skip connections yielded a 5% average accuracy boost. ConclusionThis study effectively combined knowledge-driven and data-driven techniques to enhance MRI brain segmentation stability and accuracy, thereby demonstrating strong potential for clinical application in managing intensity inhomogeneity in data-constrained settings. Magnetic resonance imaging (MRI) brain tissue segmentation is essential for the diagnosis and treatment of neurological diseases; however, intensity inhomogeneity poses a significant challenge, particularly in data-limited scenarios. This study aimed to explore an algorithm designed to robustly address intensity inhomogeneity for brain MRI tissue segmentation under dataset constraints. We propose a two-stage framework that leverages data-driven and knowledge-driven approaches. Initially, a data-driven model was employed for skull-stripping, where a lightweight module, named multi-view dilated convolution attention (MDCA), is integrated into skip connections. The MDCA module eliminates the effect of intensity inhomogeneity enriched in shallow features at multiple scales, thus avoiding the negative impact on deeper abstract features. Furthermore, we introduced multiplicative and additive intrinsic components optimization (MAICO) algorithm, which decomposes MRI images into their real anatomical structures, multiplicative and additive bias fields, and zero-mean Gaussian noise, thus enabling precise anatomical segmentation. Experiments on MRBrainS13 and MRBrainS18 public datasets involved the random introduction of intensity inhomogeneity to generate training, validation, and testing sets with 60%, 20%, and 20% splits, respectively. Segmentation performance was measured using Dice coefficients and compared to methods such as MICO, FSL, and UNet. An ablation study further validated the efficacy of the MDCA module. Our approach improved MRI brain tissue segmentation accuracy, achieving a mean Dice coefficient of 0.7733 across tissue types. With MDCA and MAICO, it reached 0.8163 for white matter, 0.7402 for gray matter, and 0.7634 for cerebrospinal fluid, outperforming other algorithms. Additionally, MDCA module integration in skip connections yielded a 5% average accuracy boost. This study effectively combined knowledge-driven and data-driven techniques to enhance MRI brain segmentation stability and accuracy, thereby demonstrating strong potential for clinical application in managing intensity inhomogeneity in data-constrained settings. |
| Author | Li, Chunming Tang, Ling Wang, Pengxiang Peng, Yuliang Huang, Furong Shen, Zijie Yang, Lan Liu, Jun Shao, Lizhi Pu, Mingshu |
| Author_xml | – sequence: 1 givenname: Jun surname: Liu fullname: Liu, Jun organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 2 givenname: Yuliang surname: Peng fullname: Peng, Yuliang organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 3 givenname: Mingshu surname: Pu fullname: Pu, Mingshu organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 4 givenname: Ling surname: Tang fullname: Tang, Ling organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 5 givenname: Lizhi surname: Shao fullname: Shao, Lizhi organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 6 givenname: Pengxiang surname: Wang fullname: Wang, Pengxiang organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 7 givenname: Lan surname: Yang fullname: Yang, Lan organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 8 givenname: Furong surname: Huang fullname: Huang, Furong organization: Department of Endocrinology, Nanjiang People’s Hospital, Bazhong, Sichuan 636600, China – sequence: 9 givenname: Zijie surname: Shen fullname: Shen, Zijie email: 202312012436@std.uestc.edu.cn organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China – sequence: 10 givenname: Chunming surname: Li fullname: Li, Chunming organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China |
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| Cites_doi | 10.3390/ijgi10040256 10.1109/42.845174 10.1016/j.neuroimage.2011.09.015 10.1006/cviu.2001.0951 10.1109/CVPRW.2015.7301312 10.1016/j.irbm.2022.02.005 10.1109/42.585758 10.1016/j.artmed.2023.102608 10.1109/42.811268 10.1016/j.imed.2021.03.003 10.1016/j.job.2022.03.003 10.1155/2015/813696 10.1109/ACCESS.2022.3175188 10.1016/j.compmedimag.2022.102157 10.1007/978-3-319-24574-4_28 10.1109/TTS.2023.3234203 10.1016/j.mri.2018.08.024 10.5121/ijma.2013.5102 10.1109/TMI.2019.2937271 10.1007/978-3-030-01234-2_49 10.1049/iet-ipr.2018.5171 10.1109/TCYB.2018.2830977 10.1109/TMI.2016.2548501 10.1109/TMI.2006.891486 10.1016/j.patcog.2021.108420 10.1109/42.802752 10.1016/j.compbiomed.2022.106472 10.1080/03772063.2017.1409088 10.1016/j.imed.2023.10.001 10.1016/j.mri.2014.03.010 10.1016/j.mri.2019.06.010 10.1109/34.295913 10.1109/42.511747 10.1016/j.jneumeth.2021.109091 |
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| Keywords | Deep learning Brain segmentation Attention mechanism Intensity inhomogeneity Magnetic resonance imaging |
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| References | Jenkinson, Beckmann, Behrens, Woolrich, Smith (bib0035) 2012; 62 Yang, Wang, Feng (bib0019) 2019; 13 Clèrigues, Valverde, Salvi, Oliver, Lladó (bib0023) 2023; 103 Ronneberger O., Fischer P., Brox T.. U-net: Convolutional networks for biomedical image segmentation. 2015.. 1505.04597. 10.48550/arXiv.1505.04597 Styner, Brechbuhler, Szckely, Gerig (bib0014) 2000; 19 Gudise, Kande, Satya Savithri (bib0004) 2019; 65 Moeskops, Viergever, Mendrik, De Vries, Benders, Isgum (bib0027) 2016; 35 K, T (bib0010) 2013; 5 Guillemaud, Brady (bib0013) 1997; 16 Wei, Wu, Wang, Bui, Qu, Yap (bib0026) 2022; 124 Wells, Grimson, Kikinis, Jolesz (bib0012) 1996; 15 Khosravanian, Rahmanimanesh, Keshavarzi (bib0005) 2021; 352 Li, Gore, Davatzikos (bib0018) 2014; 32 Adams, Bischof (bib0009) 1994; 16 Vovk, Pernus, Likar (bib0016) 2007; 26 Beucher, Lantuéjoul (bib0008) 1979; vol. 132 Halder, Talukdar (bib0001) 2019; 62 Lee, Lee, Lee, Kim, Lim, Kim (bib0024) 2023; 153 Mendrik, Vincken, Kuijf, Breeuwer, Bouvy, De Bresser (bib0032) 2015; 2015 Yang, Ruan, Wu (bib0020) 2018; 54 Chen L.-C., Zhu Y., Papandreou G., Schroff F., Adam H.. Encoder-decoder with atrous separable convolution for semantic image segmentation. 2018.. 10.48550/ARXIV.1802.02611 Ciceri, Squarcina, Giubergia, Bertoldo, Brambilla, Peruzzo (bib0022) 2023; 143 Ahishakiye, Van Gijzen, Tumwiine, Wario, Obungoloch (bib0030) 2021; 1 Rahali, Dridi, Ben Salem, Descombes, Debreuve, De Graeve (bib0011) 2022; 43 Bai, Zhang, Liu (bib0003) 2019; 49 Ponciano, Roetner, Reiterer (bib0006) 2021; 10 Kumar, Jha, Kumar, Raju (bib0021) 2024; 4 Dhar, Dey, Borra, Sherratt (bib0029) 2023; 4 Forgy (bib0007) 1965; 21 Dayananda, Choi, Lee (bib0002) 2022; 10 Paszke A., Gross S., Chintala S., Chanan G., Yang E., DeVito Z., et al. Automatic differentiation in pytorch2017. Pham (bib0017) 2001; 84 De Brebisson, Montana (bib0025) 2015 Pham, Prince (bib0015) Sept./1999; 18 Sun, Ma, Ding, Huang, Liang, Paisley (bib0028) 2020; 39 Van Leemput, Maes, Vandermeulen, Suetens (bib0033) 1999; 18 Tsuneki (bib0031) 2022; 64 Adams (10.1016/j.imed.2025.02.002_bib0009) 1994; 16 Styner (10.1016/j.imed.2025.02.002_bib0014) 2000; 19 Bai (10.1016/j.imed.2025.02.002_bib0003) 2019; 49 Khosravanian (10.1016/j.imed.2025.02.002_bib0005) 2021; 352 Van Leemput (10.1016/j.imed.2025.02.002_bib0033) 1999; 18 Pham (10.1016/j.imed.2025.02.002_bib0015) 1999; 18 Dayananda (10.1016/j.imed.2025.02.002_bib0002) 2022; 10 Dhar (10.1016/j.imed.2025.02.002_bib0029) 2023; 4 Sun (10.1016/j.imed.2025.02.002_bib0028) 2020; 39 Mendrik (10.1016/j.imed.2025.02.002_bib0032) 2015; 2015 Forgy (10.1016/j.imed.2025.02.002_bib0007) 1965; 21 Rahali (10.1016/j.imed.2025.02.002_bib0011) 2022; 43 Gudise (10.1016/j.imed.2025.02.002_bib0004) 2019; 65 Ciceri (10.1016/j.imed.2025.02.002_bib0022) 2023; 143 K (10.1016/j.imed.2025.02.002_bib0010) 2013; 5 Clèrigues (10.1016/j.imed.2025.02.002_bib0023) 2023; 103 Li (10.1016/j.imed.2025.02.002_bib0018) 2014; 32 De Brebisson (10.1016/j.imed.2025.02.002_bib0025) 2015 Halder (10.1016/j.imed.2025.02.002_bib0001) 2019; 62 Wei (10.1016/j.imed.2025.02.002_bib0026) 2022; 124 Jenkinson (10.1016/j.imed.2025.02.002_bib0035) 2012; 62 Ahishakiye (10.1016/j.imed.2025.02.002_bib0030) 2021; 1 Tsuneki (10.1016/j.imed.2025.02.002_bib0031) 2022; 64 Kumar (10.1016/j.imed.2025.02.002_bib0021) 2024; 4 Yang (10.1016/j.imed.2025.02.002_bib0020) 2018; 54 Wells (10.1016/j.imed.2025.02.002_bib0012) 1996; 15 Moeskops (10.1016/j.imed.2025.02.002_bib0027) 2016; 35 Yang (10.1016/j.imed.2025.02.002_bib0019) 2019; 13 Ponciano (10.1016/j.imed.2025.02.002_bib0006) 2021; 10 Guillemaud (10.1016/j.imed.2025.02.002_bib0013) 1997; 16 10.1016/j.imed.2025.02.002_bib0034 Pham (10.1016/j.imed.2025.02.002_bib0017) 2001; 84 Vovk (10.1016/j.imed.2025.02.002_bib0016) 2007; 26 10.1016/j.imed.2025.02.002_bib0036 10.1016/j.imed.2025.02.002_bib0037 Beucher (10.1016/j.imed.2025.02.002_bib0008) 1979; vol. 132 Lee (10.1016/j.imed.2025.02.002_bib0024) 2023; 153 |
| References_xml | – volume: 65 start-page: 250 year: 2019 end-page: 262 ident: bib0004 article-title: Segmentation of MR images of the brain to detect WM, GM, and CSF tissues in the presence of noise and intensity inhomogeneity publication-title: IETE Journal of Research – volume: 18 start-page: 737 year: Sept./1999 end-page: 752 ident: bib0015 article-title: Adaptive fuzzy segmentation of magnetic resonance images publication-title: IEEE Trans Med Imag – volume: 39 start-page: 898 year: 2020 end-page: 909 ident: bib0028 article-title: A 3d spatially weighted network for segmentation of brain tissue from MRI publication-title: IEEE Trans Med Imag – volume: 4 start-page: 68 year: 2023 end-page: 75 ident: bib0029 article-title: Challenges of deep learning in medical image analysis—improving explainability and trust publication-title: IEEE Transactions on Technology and Society – volume: 143 year: 2023 ident: bib0022 article-title: Review on deep learning fetal brain segmentation from magnetic resonance images publication-title: Artif Intell Med – reference: Paszke A., Gross S., Chintala S., Chanan G., Yang E., DeVito Z., et al. Automatic differentiation in pytorch2017. – volume: 26 start-page: 405 year: 2007 end-page: 421 ident: bib0016 article-title: A review of methods for correction of intensity inhomogeneity in MRI publication-title: IEEE Trans Med Imag – volume: 4 start-page: 161 year: 2024 end-page: 169 ident: bib0021 article-title: Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging publication-title: Intelligent Medicine – volume: 43 start-page: 640 year: 2022 end-page: 657 ident: bib0011 article-title: Biological image segmentation using region-scalable fitting energy with b-spline level set implementation and watershed publication-title: IRBM – volume: 18 start-page: 885 year: 1999 end-page: 896 ident: bib0033 article-title: Automated model-based bias field correction of MR images of the brain publication-title: IEEE Trans Med Imag – volume: 16 start-page: 238 year: 1997 end-page: 251 ident: bib0013 article-title: Estimating the bias field of MR images publication-title: IEEE Trans Med Imag – volume: 352 year: 2021 ident: bib0005 article-title: A level set method based on domain transformation and bias correction for MRI brain tumor segmentation publication-title: J Neurosci Methods – volume: 1 start-page: 118 year: 2021 end-page: 127 ident: bib0030 article-title: A survey on deep learning in medical image reconstruction publication-title: Intelligent Medicine – volume: 153 year: 2023 ident: bib0024 article-title: Fine-grained brain tissue segmentation for brain modeling of stroke patient publication-title: Comput Biol Med – volume: 84 start-page: 285 year: 2001 end-page: 297 ident: bib0017 article-title: Spatial models for fuzzy clustering publication-title: Comput Vision Image Understanding – reference: Chen L.-C., Zhu Y., Papandreou G., Schroff F., Adam H.. Encoder-decoder with atrous separable convolution for semantic image segmentation. 2018.. 10.48550/ARXIV.1802.02611 – volume: 19 start-page: 153 year: 2000 end-page: 165 ident: bib0014 article-title: Parametric estimate of intensity inhomogeneities applied to MRI publication-title: IEEE Trans Med Imag – reference: Ronneberger O., Fischer P., Brox T.. U-net: Convolutional networks for biomedical image segmentation. 2015.. 1505.04597. 10.48550/arXiv.1505.04597 – volume: 2015 start-page: 1 year: 2015 end-page: 16 ident: bib0032 article-title: Mrbrains challenge: Online evaluation framework for brain image segmentation in 3t mri scans publication-title: Computational Intelligence and Neuroscience – volume: 103 year: 2023 ident: bib0023 article-title: Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry publication-title: Comput Med Imaging Graph – volume: 35 start-page: 1252 year: 2016 end-page: 1261 ident: bib0027 article-title: Automatic segmentation of MR brain images with a convolutional neural network publication-title: IEEE Trans Med Imag – volume: 49 start-page: 2618 year: 2019 end-page: 2630 ident: bib0003 article-title: Similarity measure-based possibilistic FCM with label information for brain MRI segmentation publication-title: IEEE Trans Cybern – volume: 21 start-page: 768 year: 1965 end-page: 769 ident: bib0007 article-title: Cluster analysis of multivariate data : efficiency versus interpretability of classifications publication-title: Biometrics – volume: 16 start-page: 641 year: 1994 end-page: 647 ident: bib0009 article-title: Seeded region growing publication-title: IEEE Trans Pattern Anal Machine Intell – volume: 124 year: 2022 ident: bib0026 article-title: A cascaded nested network for 3t brain MR image segmentation guided by 7t labeling publication-title: Pattern Recognit – start-page: 20 year: 2015 end-page: 28 ident: bib0025 article-title: Deep neural networks for anatomical brain segmentation publication-title: 2015 IEEE Conf. Comput. Vis. Pattern Recognit. Workshop CVPRW – volume: 62 start-page: 782 year: 2012 end-page: 790 ident: bib0035 article-title: Fsl publication-title: NeuroImage – volume: 13 start-page: 939 year: 2019 end-page: 945 ident: bib0019 article-title: New method for simultaneous moderate bias correction and image segmentation publication-title: IET Image Proc – volume: 54 start-page: 249 year: 2018 end-page: 264 ident: bib0020 article-title: Efficient segmentation and correction model for brain MR images with level set framework based on basis functions publication-title: Magn Reson Imaging – volume: 15 start-page: 429 year: 1996 end-page: 442 ident: bib0012 article-title: Adaptive segmentation of MRI data publication-title: IEEE Trans Med Imag – volume: 10 start-page: 256 year: 2021 ident: bib0006 article-title: Object semantic segmentation in point clouds—comparison of a deep learning and a knowledge-based method publication-title: ISPRS International Journal of Geo-Information – volume: vol. 132 year: 1979 ident: bib0008 article-title: Use of watersheds in contour detection – volume: 5 start-page: 11 year: 2013 end-page: 19 ident: bib0010 article-title: Binarization of MRI with intensity inhomogeneity using k-means clustering for segmenting hippocampus publication-title: The International journal of Multimedia & Its Applications – volume: 32 start-page: 913 year: 2014 end-page: 923 ident: bib0018 article-title: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation publication-title: Magn Reson Imaging – volume: 64 start-page: 312 year: 2022 end-page: 320 ident: bib0031 article-title: Deep learning models in medical image analysis publication-title: Journal of Oral Biosciences – volume: 62 start-page: 129 year: 2019 end-page: 151 ident: bib0001 article-title: Brain tissue segmentation using improved kernelized rough-fuzzy c-means with spatio-contextual information from MRI publication-title: Magn Reson Imaging – volume: 10 start-page: 52804 year: 2022 end-page: 52817 ident: bib0002 article-title: A squeeze u-segnet architecture based on residual convolution for brain MRI segmentation publication-title: IEEE Access – volume: 10 start-page: 256 issue: 4 year: 2021 ident: 10.1016/j.imed.2025.02.002_bib0006 article-title: Object semantic segmentation in point clouds—comparison of a deep learning and a knowledge-based method publication-title: ISPRS International Journal of Geo-Information doi: 10.3390/ijgi10040256 – volume: vol. 132 year: 1979 ident: 10.1016/j.imed.2025.02.002_bib0008 – volume: 19 start-page: 153 issue: 3 year: 2000 ident: 10.1016/j.imed.2025.02.002_bib0014 article-title: Parametric estimate of intensity inhomogeneities applied to MRI publication-title: IEEE Trans Med Imag doi: 10.1109/42.845174 – volume: 62 start-page: 782 issue: 2 year: 2012 ident: 10.1016/j.imed.2025.02.002_bib0035 article-title: Fsl publication-title: NeuroImage doi: 10.1016/j.neuroimage.2011.09.015 – volume: 84 start-page: 285 issue: 2 year: 2001 ident: 10.1016/j.imed.2025.02.002_bib0017 article-title: Spatial models for fuzzy clustering publication-title: Comput Vision Image Understanding doi: 10.1006/cviu.2001.0951 – start-page: 20 year: 2015 ident: 10.1016/j.imed.2025.02.002_bib0025 article-title: Deep neural networks for anatomical brain segmentation doi: 10.1109/CVPRW.2015.7301312 – volume: 43 start-page: 640 issue: 6 year: 2022 ident: 10.1016/j.imed.2025.02.002_bib0011 article-title: Biological image segmentation using region-scalable fitting energy with b-spline level set implementation and watershed publication-title: IRBM doi: 10.1016/j.irbm.2022.02.005 – volume: 16 start-page: 238 issue: 3 year: 1997 ident: 10.1016/j.imed.2025.02.002_bib0013 article-title: Estimating the bias field of MR images publication-title: IEEE Trans Med Imag doi: 10.1109/42.585758 – volume: 143 year: 2023 ident: 10.1016/j.imed.2025.02.002_bib0022 article-title: Review on deep learning fetal brain segmentation from magnetic resonance images publication-title: Artif Intell Med doi: 10.1016/j.artmed.2023.102608 – volume: 18 start-page: 885 issue: 10 year: 1999 ident: 10.1016/j.imed.2025.02.002_bib0033 article-title: Automated model-based bias field correction of MR images of the brain publication-title: IEEE Trans Med Imag doi: 10.1109/42.811268 – volume: 1 start-page: 118 issue: 3 year: 2021 ident: 10.1016/j.imed.2025.02.002_bib0030 article-title: A survey on deep learning in medical image reconstruction publication-title: Intelligent Medicine doi: 10.1016/j.imed.2021.03.003 – volume: 21 start-page: 768 year: 1965 ident: 10.1016/j.imed.2025.02.002_bib0007 article-title: Cluster analysis of multivariate data : efficiency versus interpretability of classifications publication-title: Biometrics – volume: 64 start-page: 312 issue: 3 year: 2022 ident: 10.1016/j.imed.2025.02.002_bib0031 article-title: Deep learning models in medical image analysis publication-title: Journal of Oral Biosciences doi: 10.1016/j.job.2022.03.003 – volume: 2015 start-page: 1 year: 2015 ident: 10.1016/j.imed.2025.02.002_bib0032 article-title: Mrbrains challenge: Online evaluation framework for brain image segmentation in 3t mri scans publication-title: Computational Intelligence and Neuroscience doi: 10.1155/2015/813696 – volume: 10 start-page: 52804 year: 2022 ident: 10.1016/j.imed.2025.02.002_bib0002 article-title: A squeeze u-segnet architecture based on residual convolution for brain MRI segmentation publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3175188 – volume: 103 year: 2023 ident: 10.1016/j.imed.2025.02.002_bib0023 article-title: Minimizing the effect of white matter lesions on deep learning based tissue segmentation for brain volumetry publication-title: Comput Med Imaging Graph doi: 10.1016/j.compmedimag.2022.102157 – ident: 10.1016/j.imed.2025.02.002_bib0036 doi: 10.1007/978-3-319-24574-4_28 – volume: 4 start-page: 68 issue: 1 year: 2023 ident: 10.1016/j.imed.2025.02.002_bib0029 article-title: Challenges of deep learning in medical image analysis—improving explainability and trust publication-title: IEEE Transactions on Technology and Society doi: 10.1109/TTS.2023.3234203 – volume: 54 start-page: 249 year: 2018 ident: 10.1016/j.imed.2025.02.002_bib0020 article-title: Efficient segmentation and correction model for brain MR images with level set framework based on basis functions publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2018.08.024 – ident: 10.1016/j.imed.2025.02.002_bib0034 – volume: 5 start-page: 11 issue: 1 year: 2013 ident: 10.1016/j.imed.2025.02.002_bib0010 article-title: Binarization of MRI with intensity inhomogeneity using k-means clustering for segmenting hippocampus publication-title: The International journal of Multimedia & Its Applications doi: 10.5121/ijma.2013.5102 – volume: 39 start-page: 898 issue: 4 year: 2020 ident: 10.1016/j.imed.2025.02.002_bib0028 article-title: A 3d spatially weighted network for segmentation of brain tissue from MRI publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2019.2937271 – ident: 10.1016/j.imed.2025.02.002_bib0037 doi: 10.1007/978-3-030-01234-2_49 – volume: 13 start-page: 939 issue: 6 year: 2019 ident: 10.1016/j.imed.2025.02.002_bib0019 article-title: New method for simultaneous moderate bias correction and image segmentation publication-title: IET Image Proc doi: 10.1049/iet-ipr.2018.5171 – volume: 49 start-page: 2618 issue: 7 year: 2019 ident: 10.1016/j.imed.2025.02.002_bib0003 article-title: Similarity measure-based possibilistic FCM with label information for brain MRI segmentation publication-title: IEEE Trans Cybern doi: 10.1109/TCYB.2018.2830977 – volume: 35 start-page: 1252 issue: 5 year: 2016 ident: 10.1016/j.imed.2025.02.002_bib0027 article-title: Automatic segmentation of MR brain images with a convolutional neural network publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2016.2548501 – volume: 26 start-page: 405 issue: 3 year: 2007 ident: 10.1016/j.imed.2025.02.002_bib0016 article-title: A review of methods for correction of intensity inhomogeneity in MRI publication-title: IEEE Trans Med Imag doi: 10.1109/TMI.2006.891486 – volume: 124 year: 2022 ident: 10.1016/j.imed.2025.02.002_bib0026 article-title: A cascaded nested network for 3t brain MR image segmentation guided by 7t labeling publication-title: Pattern Recognit doi: 10.1016/j.patcog.2021.108420 – volume: 18 start-page: 737 issue: 9 year: 1999 ident: 10.1016/j.imed.2025.02.002_bib0015 article-title: Adaptive fuzzy segmentation of magnetic resonance images publication-title: IEEE Trans Med Imag doi: 10.1109/42.802752 – volume: 153 year: 2023 ident: 10.1016/j.imed.2025.02.002_bib0024 article-title: Fine-grained brain tissue segmentation for brain modeling of stroke patient publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.106472 – volume: 65 start-page: 250 issue: 2 year: 2019 ident: 10.1016/j.imed.2025.02.002_bib0004 article-title: Segmentation of MR images of the brain to detect WM, GM, and CSF tissues in the presence of noise and intensity inhomogeneity publication-title: IETE Journal of Research doi: 10.1080/03772063.2017.1409088 – volume: 4 start-page: 161 issue: 3 year: 2024 ident: 10.1016/j.imed.2025.02.002_bib0021 article-title: Improved neurological diagnoses and treatment strategies via automated human brain tissue segmentation from clinical magnetic resonance imaging publication-title: Intelligent Medicine doi: 10.1016/j.imed.2023.10.001 – volume: 32 start-page: 913 issue: 7 year: 2014 ident: 10.1016/j.imed.2025.02.002_bib0018 article-title: Multiplicative intrinsic component optimization (MICO) for MRI bias field estimation and tissue segmentation publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2014.03.010 – volume: 62 start-page: 129 year: 2019 ident: 10.1016/j.imed.2025.02.002_bib0001 article-title: Brain tissue segmentation using improved kernelized rough-fuzzy c-means with spatio-contextual information from MRI publication-title: Magn Reson Imaging doi: 10.1016/j.mri.2019.06.010 – volume: 16 start-page: 641 issue: 6 year: 1994 ident: 10.1016/j.imed.2025.02.002_bib0009 article-title: Seeded region growing publication-title: IEEE Trans Pattern Anal Machine Intell doi: 10.1109/34.295913 – volume: 15 start-page: 429 issue: 4 year: 1996 ident: 10.1016/j.imed.2025.02.002_bib0012 article-title: Adaptive segmentation of MRI data publication-title: IEEE Trans Med Imag doi: 10.1109/42.511747 – volume: 352 year: 2021 ident: 10.1016/j.imed.2025.02.002_bib0005 article-title: A level set method based on domain transformation and bias correction for MRI brain tumor segmentation publication-title: J Neurosci Methods doi: 10.1016/j.jneumeth.2021.109091 |
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| SubjectTerms | Attention mechanism Brain segmentation Deep learning Intensity inhomogeneity Internal Medicine Magnetic resonance imaging |
| Title | Magnetic resonance imaging bias field estimation and tissue segmentation via convolutional neural networks and multiplicative and additive intrinsic components optimization |
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