Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration
Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medi...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zheng, Zhiyuan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
Multi-region medical image registration |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - London : Springer, 1993, 34(2022), 19 vom: 29. Mai, Seite 17175-17191 |
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| Übergeordnetes Werk: |
volume:34 ; year:2022 ; number:19 ; day:29 ; month:05 ; pages:17175-17191 |
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| DOI / URN: |
10.1007/s00521-022-07365-4 |
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SPR048172855 |
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| 520 | |a Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. | ||
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| 700 | 1 | |a Lian, Deliang |4 aut | |
| 700 | 1 | |a Luo, Yi |0 (orcid)0000-0002-5488-4724 |4 aut | |
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10.1007/s00521-022-07365-4 doi (DE-627)SPR048172855 (SPR)s00521-022-07365-4-e DE-627 ger DE-627 rakwb eng Zheng, Zhiyuan verfasserin (orcid)0000-0003-4920-7355 aut Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. Multi-region medical image registration (dpeaa)DE-He213 Global and local joint training strategy (dpeaa)DE-He213 Feature self-calibration network (dpeaa)DE-He213 Cao, Wenming (orcid)0000-0002-8174-6167 aut Lian, Deliang aut Luo, Yi (orcid)0000-0002-5488-4724 aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 19 vom: 29. Mai, Seite 17175-17191 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:19 day:29 month:05 pages:17175-17191 https://dx.doi.org/10.1007/s00521-022-07365-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 19 29 05 17175-17191 |
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10.1007/s00521-022-07365-4 doi (DE-627)SPR048172855 (SPR)s00521-022-07365-4-e DE-627 ger DE-627 rakwb eng Zheng, Zhiyuan verfasserin (orcid)0000-0003-4920-7355 aut Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. Multi-region medical image registration (dpeaa)DE-He213 Global and local joint training strategy (dpeaa)DE-He213 Feature self-calibration network (dpeaa)DE-He213 Cao, Wenming (orcid)0000-0002-8174-6167 aut Lian, Deliang aut Luo, Yi (orcid)0000-0002-5488-4724 aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 19 vom: 29. Mai, Seite 17175-17191 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:19 day:29 month:05 pages:17175-17191 https://dx.doi.org/10.1007/s00521-022-07365-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 19 29 05 17175-17191 |
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10.1007/s00521-022-07365-4 doi (DE-627)SPR048172855 (SPR)s00521-022-07365-4-e DE-627 ger DE-627 rakwb eng Zheng, Zhiyuan verfasserin (orcid)0000-0003-4920-7355 aut Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. Multi-region medical image registration (dpeaa)DE-He213 Global and local joint training strategy (dpeaa)DE-He213 Feature self-calibration network (dpeaa)DE-He213 Cao, Wenming (orcid)0000-0002-8174-6167 aut Lian, Deliang aut Luo, Yi (orcid)0000-0002-5488-4724 aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 19 vom: 29. Mai, Seite 17175-17191 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:19 day:29 month:05 pages:17175-17191 https://dx.doi.org/10.1007/s00521-022-07365-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 19 29 05 17175-17191 |
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10.1007/s00521-022-07365-4 doi (DE-627)SPR048172855 (SPR)s00521-022-07365-4-e DE-627 ger DE-627 rakwb eng Zheng, Zhiyuan verfasserin (orcid)0000-0003-4920-7355 aut Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. Multi-region medical image registration (dpeaa)DE-He213 Global and local joint training strategy (dpeaa)DE-He213 Feature self-calibration network (dpeaa)DE-He213 Cao, Wenming (orcid)0000-0002-8174-6167 aut Lian, Deliang aut Luo, Yi (orcid)0000-0002-5488-4724 aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 19 vom: 29. Mai, Seite 17175-17191 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:19 day:29 month:05 pages:17175-17191 https://dx.doi.org/10.1007/s00521-022-07365-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 19 29 05 17175-17191 |
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10.1007/s00521-022-07365-4 doi (DE-627)SPR048172855 (SPR)s00521-022-07365-4-e DE-627 ger DE-627 rakwb eng Zheng, Zhiyuan verfasserin (orcid)0000-0003-4920-7355 aut Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. Multi-region medical image registration (dpeaa)DE-He213 Global and local joint training strategy (dpeaa)DE-He213 Feature self-calibration network (dpeaa)DE-He213 Cao, Wenming (orcid)0000-0002-8174-6167 aut Lian, Deliang aut Luo, Yi (orcid)0000-0002-5488-4724 aut Enthalten in Neural computing & applications London : Springer, 1993 34(2022), 19 vom: 29. Mai, Seite 17175-17191 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:34 year:2022 number:19 day:29 month:05 pages:17175-17191 https://dx.doi.org/10.1007/s00521-022-07365-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 34 2022 19 29 05 17175-17191 |
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Zheng, Zhiyuan @@aut@@ Cao, Wenming @@aut@@ Lian, Deliang @@aut@@ Luo, Yi @@aut@@ |
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Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). 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Zheng, Zhiyuan |
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Zheng, Zhiyuan misc Multi-region medical image registration misc Global and local joint training strategy misc Feature self-calibration network Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration |
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Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration Multi-region medical image registration (dpeaa)DE-He213 Global and local joint training strategy (dpeaa)DE-He213 Feature self-calibration network (dpeaa)DE-He213 |
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misc Multi-region medical image registration misc Global and local joint training strategy misc Feature self-calibration network |
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feature self-calibration network with global-local training strategy for multi-region deformable medical image registration |
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Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration |
| abstract |
Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstractGer |
Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract 3D deformable medical image registration has important clinical significance. Deep learning-based methods have shown outstanding advantages in medical image registration. However, most current learning-based practices only have sound registration effects for large-area overall deformed medical images such as lung and liver CT scans. Medical images with multiple registration regions, such as brain MR scans, have become a significant shortcoming in the field of medical image registration. The current learning-based method uses only one displacement field to deform the entire moving image. For brain images with multiple registration areas, this method cannot guarantee accurate deformation of all regions, nor can it ensure the excellent maintenance of the topological structure of each part. Aiming to resolve this unresolved problem in the field of medical image registration, we propose a novel training strategy suitable for multi-region registration: global and local joint training strategy (GoLo). And then, we combine it with our proposed feature self-calibration network (FSCN) to solve the registration optimization problem of multi-region medical images. We conducted many quantitative and qualitative evaluation tests on the public brain MR scan data set(OASIS). The test results show that our method can further improve the accuracy of the registered image and ensure reversibility compared with the existing state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2022 |
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Feature self-calibration network with global-local training strategy for multi-region deformable medical image registration |
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https://dx.doi.org/10.1007/s00521-022-07365-4 |
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Cao, Wenming Lian, Deliang Luo, Yi |
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10.1007/s00521-022-07365-4 |
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2024-07-03T17:30:14.372Z |
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| score |
7.4023743 |