Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network

The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, exis...

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Vydáno v:	Journal of medical systems Ročník 44; číslo 1; s. 15 - 7
Hlavní autoři:	Liu, Jian, Wang, Jian, Ruan, Weiwei, Lin, Chengshan, Chen, Daguo
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Springer US 01.01.2020 Springer Nature B.V
Témata:	Algorithms Biomedical materials Bone density Bone diseases Bone mineral density Deep learning Density measurement Diagnosis Diagnostic systems Distributed Analytics and Deep Learning in Health Care Entropy (Information theory) Feature maps Health Informatics Health Sciences Image segmentation Medical diagnosis Medical imaging Medicine Medicine & Public Health Normalizing Object recognition Osteoporosis Patient Facing Systems Statistics for Life Sciences Deep learning Osteoporosis X-ray image Bone mineral density Medical diagnosis U-net model
ISSN:	0148-5598, 1573-689X, 1573-689X
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Abstract	The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods.
AbstractList	The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods. The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods.The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods.
ArticleNumber	15
Author	Lin, Chengshan Liu, Jian Chen, Daguo Wang, Jian Ruan, Weiwei
Author_xml	– sequence: 1 givenname: Jian surname: Liu fullname: Liu, Jian organization: Department of Orthopedics, Tongde Hospital of Zhejiang Province – sequence: 2 givenname: Jian surname: Wang fullname: Wang, Jian organization: Department of Orthopedics, Tongde Hospital of Zhejiang Province – sequence: 3 givenname: Weiwei surname: Ruan fullname: Ruan, Weiwei organization: Department of Orthopedics, Tongde Hospital of Zhejiang Province – sequence: 4 givenname: Chengshan surname: Lin fullname: Lin, Chengshan organization: Department of Orthopedics, Tongde Hospital of Zhejiang Province – sequence: 5 givenname: Daguo surname: Chen fullname: Chen, Daguo email: chendg@zjlantone.com organization: Hangzhou Lantone Information Technology Co., LTD
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CitedBy_id	crossref_primary_10_1186_s12891_024_08120_7 crossref_primary_10_1016_j_jdent_2025_105650 crossref_primary_10_1186_s12880_022_00868_5 crossref_primary_10_1007_s10489_022_03682_2 crossref_primary_10_1007_s11657_021_00985_8 crossref_primary_10_1007_s13369_021_05339_7 crossref_primary_10_1088_1755_1315_632_4_042008 crossref_primary_10_1016_j_artmed_2023_102639 crossref_primary_10_1007_s10462_023_10638_6 crossref_primary_10_1155_2021_4196241 crossref_primary_10_1016_j_bspc_2023_104828 crossref_primary_10_1007_s11042_022_13911_y crossref_primary_10_1007_s11517_020_02171_3 crossref_primary_10_3390_s20174979 crossref_primary_10_1177_03000605241244754 crossref_primary_10_2196_40179 crossref_primary_10_3233_MGS_230123 crossref_primary_10_1002_jbmr_4292 crossref_primary_10_1016_j_bone_2024_117317 crossref_primary_10_3390_app13158758
Cites_doi	10.1507/endocrj.EJ17-0331 10.1186/s12891-017-1718-7 10.1016/j.mcm.2008.12.024 10.1109/TCYB.2014.2330853 10.1111/1756-185X.12590 10.1109/TCYB.2014.2334595 10.1109/TNSRE.2017.2748388 10.1016/j.bone.2017.02.013 10.1109/TCYB.2015.2399351 10.1109/ACCESS.2018.2825352 10.1109/TFUZZ.2016.2637405 10.3349/ymj.2013.54.6.1321 10.1007/s10916-018-1116-1 10.1007/s10334-016-0532-9 10.1109/TNNLS.2015.2511179 10.1016/j.knosys.2017.05.018 10.1109/ACCESS.2019.2929270 10.5603/EP.2017.0062 10.3813/AAA.919072 10.1109/TMI.2019.2935916 10.1109/ATSIP.2017.8075537 10.1016/j.rbre.2017.07.001 10.1145/2975167.2975208
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References	Qian, Zhao, Jiang, Kuan-Hao, Deng, Wang, Muzic (CR3) 2017; 130 Keun, Kean, Won (CR28) 2013; 54 Abid, Pereira, Fernandes (CR18) 2017; 103 CR15 CR14 Mcclung, Baron, Bouxsein (CR12) 2017; 98 Nakamoto, Taguchi, Verdonschot (CR17) 2018; 24 Xia, Yin, Qian, Jiang, Wang (CR9) 2019; 7 Qian, Zhou, Jiang, Liang, Zhao, Wang, Kuan-Hao, Muzic (CR4) 2018; 6 Chou, Leboff (CR16) 2017; 12 Peng, Yi, Deng (CR1) 2016; 46 Ordóñez, Matías, de Cos Juez (CR26) 2009; 50 Jiang, Chung, Wang, Deng, Wang, Qian (CR7) 2015; 45 Xia, Yin, Zhang (CR10) 2019; 43 Grover, Bachrach (CR13) 2017; 23 Malochet-Guinamand, Pereira, Tatar (CR19) 2017; 18 Jiang, Wu, Deng, Qian, Wang, Wang, Chung, Choi, Wang (CR5) 2017; 25 Kulkarni, Gaonkar, Vijayarajan (CR24) 2017; 12 CR29 CR25 Salehi-Abari (CR21) 2017; 20 CR23 Qian, Jiang, Wang, Kuan-Hao, Wang, Hu, Muzic, Jr. (CR2) 2017; 28 Oka, Ohira, Suzuki (CR22) 2017; 65 CR20 Lorenc, Głuszko, Franek, Jabłoński, Jaworski, Kalinka-Warzocha, Karczmarewicz, Kostka, Księzopolska-Orłowska, Marcinowska-Suchowierska, Misiorowski, Więcek (CR11) 2017; 68 Jiang, Chung, Ishibuchi (CR8) 2015; 45 Jiang, Deng, Chung, Wang, Qian, Choi, Wang (CR6) 2017; 25 Pedoia, Majumdar, Link (CR27) 2016; 29 M Mcclung (1502_CR12) 2017; 98 1502_CR15 1502_CR14 P Qian (1502_CR4) 2018; 6 YT Keun (1502_CR28) 2013; 54 SH Chou (1502_CR16) 2017; 12 Y Jiang (1502_CR7) 2015; 45 M Grover (1502_CR13) 2017; 23 S Malochet-Guinamand (1502_CR19) 2017; 18 A Abid (1502_CR18) 2017; 103 I Salehi-Abari (1502_CR21) 2017; 20 C Ordóñez (1502_CR26) 2009; 50 Y Jiang (1502_CR5) 2017; 25 Q Peng (1502_CR1) 2016; 46 T Nakamoto (1502_CR17) 2018; 24 1502_CR29 R Lorenc (1502_CR11) 2017; 68 1502_CR25 1502_CR23 1502_CR20 Y Jiang (1502_CR8) 2015; 45 K Xia (1502_CR10) 2019; 43 R Oka (1502_CR22) 2017; 65 KR Kulkarni (1502_CR24) 2017; 12 Y Jiang (1502_CR6) 2017; 25 K Xia (1502_CR9) 2019; 7 P Qian (1502_CR3) 2017; 130 P Qian (1502_CR2) 2017; 28 V Pedoia (1502_CR27) 2016; 29
References_xml	– volume: 65 start-page: 193 issue: 2 year: 2017 end-page: 202 ident: CR22 article-title: Fracture risk assessment tool (FRAX) and for the diagnosis of osteoporosis in Japanese middle-aged and elderly women: Chiba bone survey[J] publication-title: Endocrine Journal doi: 10.1507/endocrj.EJ17-0331 – volume: 23 start-page: 98 issue: 08 year: 2017 end-page: 107 ident: CR13 article-title: Osteoporosis in children with chronic illnesses: Diagnosis, monitoring, and treatment[J] publication-title: Current Osteoporosis Reports – volume: 18 start-page: 357 issue: 1 year: 2017 end-page: 369 ident: CR19 article-title: Prevalence and risk factors of low bone mineral density in spondyloarthritis and prevalence of vertebral fractures[J] publication-title: BMC Musculoskeletal Disorders doi: 10.1186/s12891-017-1718-7 – volume: 50 start-page: 673 issue: 5–6 year: 2009 end-page: 679 ident: CR26 article-title: Machine learning techniques applied to the determination of osteoporosis incidence in post-menopausal women[J] publication-title: Mathematical and Computer Modelling doi: 10.1016/j.mcm.2008.12.024 – volume: 45 start-page: 548 issue: 3 year: 2015 end-page: 561 ident: CR8 article-title: Multitask TSK fuzzy system modeling by mining intertask common hidden structure publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2330853 – ident: CR14 – volume: 20 start-page: 122 issue: 1 year: 2017 end-page: 125 ident: CR21 article-title: Early diagnosis of osteopenia/osteoporosis by bone mineral density test using DXA method in early adulthood-a pre-emptive step towards future bone health[J] publication-title: International Journal of Rheumatic Diseases doi: 10.1111/1756-185X.12590 – volume: 12 start-page: 263 issue: 1 year: 2017 end-page: 277 ident: CR24 article-title: Analysis of lower back pain disorder using deep learning[J] publication-title: IOP Conference Series Materials Science and Engineering – volume: 45 start-page: 688 issue: 4 year: 2015 end-page: 701 ident: CR7 article-title: Collaborative fuzzy clustering from multiple weighted views publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2334595 – ident: CR29 – volume: 25 start-page: 2270 issue: 12 year: 2017 end-page: 2284 ident: CR5 article-title: Seizure Classification from EEG Signals using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System publication-title: IEEE Trans. Neural Systems & Rehabilitation Engineering doi: 10.1109/TNSRE.2017.2748388 – ident: CR25 – ident: CR23 – volume: 98 start-page: 37 year: 2017 ident: CR12 article-title: An update on osteoporosis pathogenesis, diagnosis, and treatment[J] publication-title: Bone doi: 10.1016/j.bone.2017.02.013 – volume: 46 start-page: 181 issue: 1 year: 2016 end-page: 193 ident: CR1 article-title: Cluster prototypes and fuzzy memberships jointly leveraged cross-domain maximum entropy clustering publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2015.2399351 – volume: 6 start-page: 28594 year: 2018 end-page: 28610 ident: CR4 article-title: Multi-view maximum entropy clustering by jointly leveraging inter-view collaborations and intra-view-weighted attributes publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2825352 – volume: 25 start-page: 3 issue: 1 year: 2017 end-page: 20 ident: CR6 article-title: Recognition of epileptic EEG signals using a novel multi-view TSK fuzzy system publication-title: IEEE Trans. Fuzzy Systems doi: 10.1109/TFUZZ.2016.2637405 – volume: 54 start-page: 1321 issue: 6 year: 2013 end-page: 1332 ident: CR28 article-title: Osteoporosis risk prediction for bone mineral density assessment of postmenopausal women using machine learning[J] publication-title: Yonsei Medical Journal doi: 10.3349/ymj.2013.54.6.1321 – volume: 43 start-page: 2:1 issue: 1 year: 2019 end-page: 2:12 ident: CR10 article-title: Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm publication-title: J. Medical Systems doi: 10.1007/s10916-018-1116-1 – ident: CR15 – volume: 29 start-page: 207 issue: 2 year: 2016 end-page: 221 ident: CR27 article-title: Segmentation of joint and musculoskeletal tissue in the study of arthritis[J] publication-title: Magnetic Resonance Materials in Physics, Biology and Medicine doi: 10.1007/s10334-016-0532-9 – volume: 28 start-page: 1123 issue: 5 year: 2017 end-page: 1138 ident: CR2 article-title: Affinity and penalty jointly constrained spectral clustering with all-compatibility, flexibility, and robustness publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2511179 – volume: 130 start-page: 33 year: 2017 end-page: 50 ident: CR3 article-title: Knowledge-leveraged transfer fuzzy c-means for texture image segmentation with self-adaptive cluster prototype matching publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2017.05.018 – volume: 7 start-page: 96349 year: 2019 end-page: 96358 ident: CR9 article-title: Liver semantic segmentation algorithm based on improved deep adversarial networks in combination of weighted loss function on abdominal CT images publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2929270 – volume: 68 start-page: 604 issue: 5 year: 2017 end-page: 614 ident: CR11 article-title: Guidelines for the diagnosis and management of osteoporosis in Poland : Update 2017[J] publication-title: Endokrynologia Polska doi: 10.5603/EP.2017.0062 – volume: 103 start-page: 421 issue: 3 year: 2017 end-page: 429 ident: CR18 article-title: Sensitivity study of ultrasonic guided waves to cortical bone mechanical properties with axial and circumferential propagation[J] publication-title: Acta Acustica united with Acustica doi: 10.3813/AAA.919072 – volume: 24 start-page: 2108 issue: 22 year: 2018 end-page: 2117 ident: CR17 article-title: Improvement of region of interest extraction and scanning method of computer-aided diagnosis system for osteoporosis using panoramic radiographs[J] publication-title: Oral Radiology – volume: 12 start-page: 108 issue: 2 year: 2017 end-page: 117 ident: CR16 article-title: Vertebral imaging in the diagnosis of osteoporosis: A Clinician’s perspective[J] publication-title: Current Osteoporosis Reports – ident: CR20 – ident: 1502_CR14 – volume: 23 start-page: 98 issue: 08 year: 2017 ident: 1502_CR13 publication-title: Current Osteoporosis Reports – ident: 1502_CR25 doi: 10.1109/TMI.2019.2935916 – volume: 98 start-page: 37 year: 2017 ident: 1502_CR12 publication-title: Bone doi: 10.1016/j.bone.2017.02.013 – volume: 46 start-page: 181 issue: 1 year: 2016 ident: 1502_CR1 publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2015.2399351 – volume: 20 start-page: 122 issue: 1 year: 2017 ident: 1502_CR21 publication-title: International Journal of Rheumatic Diseases doi: 10.1111/1756-185X.12590 – ident: 1502_CR15 doi: 10.1109/ATSIP.2017.8075537 – volume: 65 start-page: 193 issue: 2 year: 2017 ident: 1502_CR22 publication-title: Endocrine Journal doi: 10.1507/endocrj.EJ17-0331 – volume: 130 start-page: 33 year: 2017 ident: 1502_CR3 publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2017.05.018 – volume: 68 start-page: 604 issue: 5 year: 2017 ident: 1502_CR11 publication-title: Endokrynologia Polska doi: 10.5603/EP.2017.0062 – volume: 25 start-page: 2270 issue: 12 year: 2017 ident: 1502_CR5 publication-title: IEEE Trans. Neural Systems & Rehabilitation Engineering doi: 10.1109/TNSRE.2017.2748388 – volume: 12 start-page: 108 issue: 2 year: 2017 ident: 1502_CR16 publication-title: Current Osteoporosis Reports – volume: 28 start-page: 1123 issue: 5 year: 2017 ident: 1502_CR2 publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2015.2511179 – volume: 43 start-page: 2:1 issue: 1 year: 2019 ident: 1502_CR10 publication-title: J. Medical Systems doi: 10.1007/s10916-018-1116-1 – ident: 1502_CR20 doi: 10.1016/j.rbre.2017.07.001 – volume: 18 start-page: 357 issue: 1 year: 2017 ident: 1502_CR19 publication-title: BMC Musculoskeletal Disorders doi: 10.1186/s12891-017-1718-7 – volume: 50 start-page: 673 issue: 5–6 year: 2009 ident: 1502_CR26 publication-title: Mathematical and Computer Modelling doi: 10.1016/j.mcm.2008.12.024 – ident: 1502_CR23 doi: 10.1145/2975167.2975208 – volume: 29 start-page: 207 issue: 2 year: 2016 ident: 1502_CR27 publication-title: Magnetic Resonance Materials in Physics, Biology and Medicine doi: 10.1007/s10334-016-0532-9 – volume: 25 start-page: 3 issue: 1 year: 2017 ident: 1502_CR6 publication-title: IEEE Trans. Fuzzy Systems doi: 10.1109/TFUZZ.2016.2637405 – volume: 24 start-page: 2108 issue: 22 year: 2018 ident: 1502_CR17 publication-title: Oral Radiology – volume: 103 start-page: 421 issue: 3 year: 2017 ident: 1502_CR18 publication-title: Acta Acustica united with Acustica doi: 10.3813/AAA.919072 – volume: 45 start-page: 548 issue: 3 year: 2015 ident: 1502_CR8 publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2330853 – volume: 7 start-page: 96349 year: 2019 ident: 1502_CR9 publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2929270 – volume: 12 start-page: 263 issue: 1 year: 2017 ident: 1502_CR24 publication-title: IOP Conference Series Materials Science and Engineering – volume: 6 start-page: 28594 year: 2018 ident: 1502_CR4 publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2825352 – volume: 45 start-page: 688 issue: 4 year: 2015 ident: 1502_CR7 publication-title: IEEE Transactions on Cybernetics doi: 10.1109/TCYB.2014.2334595 – ident: 1502_CR29 – volume: 54 start-page: 1321 issue: 6 year: 2013 ident: 1502_CR28 publication-title: Yonsei Medical Journal doi: 10.3349/ymj.2013.54.6.1321
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SubjectTerms	Algorithms Biomedical materials Bone density Bone diseases Bone mineral density Deep learning Density measurement Diagnosis Diagnostic systems Distributed Analytics and Deep Learning in Health Care Entropy (Information theory) Feature maps Health Informatics Health Sciences Image segmentation Medical diagnosis Medical imaging Medicine Medicine & Public Health Normalizing Object recognition Osteoporosis Patient Facing Systems Statistics for Life Sciences
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Title	Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network
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