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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Veröffentlicht in:	Journal of medical systems Jg. 44; H. 1; S. 15 - 7
Hauptverfasser:	Liu, Jian, Wang, Jian, Ruan, Weiwei, Lin, Chengshan, Chen, Daguo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2020 Springer Nature B.V
Schlagworte:	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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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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