Greendale GA, Barrett-Connor E, Ingles S, Haile R (1995) Late physical and functional effects of osteoporotic fracture in women: the Rancho Bernardo Study. J Am Geriatr Soc 43(9):955–961. https://doi.org/10.1111/j.1532-5415.1995.tb05557.x
Article CAS PubMed Google Scholar
Lee DG, Bae JH (2023) Fatty infiltration of the multifidus muscle independently increases osteoporotic vertebral compression fracture risk. BMC Musculoskelet Disord 24(1):508. https://doi.org/10.1186/s12891-023-06640-2
Article CAS PubMed PubMed Central Google Scholar
Jin C, Xu G, Weng D, Xie M, Qian Y (2018) Impact of magnetic resonance imaging on treatment-related decision making for osteoporotic vertebral Compression fracture: a prospective Randomized Trial. Med Sci Monit 24:50–57. https://doi.org/10.12659/msm.905729
Article PubMed PubMed Central Google Scholar
Cheng J, Muheremu A, Zeng X, Liu L, Liu Y, Chen Y (2019) Percutaneous vertebroplasty vs balloon kyphoplasty in the treatment of newly onset osteoporotic vertebral compression fractures: a retrospective cohort study. Med (Baltim) 98(10):e14793. https://doi.org/10.1097/md.0000000000014793
Palmowski Y, Balmer S, Hu Z, Winkler T, Schnake KJ, Kandziora F et al (2022) Relationship between the OF classification and Radiological Outcome of osteoporotic vertebral fractures after Kyphoplasty. Global Spine J 12(4):646–653. https://doi.org/10.1177/2192568220964051
Yang H, Yan S, Li J, Zheng X, Yao Q, Duan S et al (2022) Prediction of acute versus chronic osteoporotic vertebral fracture using radiomics-clinical model on CT. Eur J Radiol 149:110197. https://doi.org/10.1016/j.ejrad.2022.110197
Suzuki N, Ogikubo O, Hansson T (2009) The prognosis for pain, disability, activities of daily living and quality of life after an acute osteoporotic vertebral body fracture: its relation to fracture level, type of fracture and grade of fracture deformation. Eur Spine J 18(1):77–88. https://doi.org/10.1007/s00586-008-0847-y
Choi WH, Oh SH, Lee CJ, Rhim JK, Chung BS, Hong HJ (2012) Usefulness of SPAIR Image, Fracture Line and the adjacent discs change on magnetic resonance image in the Acute Osteoporotic Compression fracture. Korean J Spine 9(3):227–231. https://doi.org/10.14245/kjs.2012.9.3.227
Article PubMed PubMed Central Google Scholar
Marongiu G, Congia S, Verona M, Lombardo M, Podda D, Capone A (2018) The impact of magnetic resonance imaging in the diagnostic and classification process of osteoporotic vertebral fractures. Injury 49(Suppl 3):S26–s31. https://doi.org/10.1016/j.injury.2018.10.006
Bierry G, Venkatasamy A, Kremer S, Dosch JC, Dietemann JL (2014) Dual-energy CT in vertebral compression fractures: performance of visual and quantitative analysis for bone marrow edema demonstration with comparison to MRI. Skeletal Radiol 43(4):485–492. https://doi.org/10.1007/s00256-013-1812-3
Kim AY, Yoon MA, Ham SJ, Cho YC, Ko Y, Park B et al (2022) Prediction of the acuity of Vertebral Compression fractures on CT using Radiologic and Radiomic features. Acad Radiol 29(10):1512–1520. https://doi.org/10.1016/j.acra.2021.12.008
Ono Y, Suzuki N, Sakano R, Kikuchi Y, Kimura T, Sutherland K et al (2023) A deep learning-based model for classifying osteoporotic lumbar vertebral fractures on radiographs: a retrospective Model Development and Validation Study. J Imaging 9(9). https://doi.org/10.3390/jimaging9090187
Frighetto-Pereira L, Rangayyan RM, Metzner GA, de Azevedo-Marques PM, Nogueira-Barbosa MH (2016) Shape, texture and statistical features for classification of benign and malignant vertebral compression fractures in magnetic resonance images. Comput Biol Med 73:147–156. https://doi.org/10.1016/j.compbiomed.2016.04.006
Zhang J, Liu J, Liang Z, Xia L, Zhang W, Xing Y et al (2023) Differentiation of acute and chronic vertebral compression fractures using conventional CT based on deep transfer learning features and hand-crafted radiomics features. BMC Musculoskelet Disord 24(1):165. https://doi.org/10.1186/s12891-023-06281-5
Article PubMed PubMed Central Google Scholar
Hallinan J, Zhu L, Yang K, Makmur A, Algazwi DAR, Thian YL et al (2021) Deep learning model for automated detection and classification of Central Canal, lateral recess, and neural Foraminal stenosis at lumbar spine MRI. Radiology 300(1):130–138. https://doi.org/10.1148/radiol.2021204289
Park JE, Park SY, Kim HJ, Kim HS (2019) Reproducibility and generalizability in Radiomics modeling: possible strategies in radiologic and statistical perspectives. Korean J Radiol 20(7):1124–1137. https://doi.org/10.3348/kjr.2018.0070
Article PubMed PubMed Central Google Scholar
Cao Y, Yu J, Zhang H, Xiong J, Luo Z (2022) Classification of hepatic cavernous hemangioma or hepatocellular carcinoma using a convolutional neural network model. J Gastrointest Oncol 13(2):787–791. https://doi.org/10.21037/jgo-22-197
Article PubMed PubMed Central Google Scholar
Zhu G, Luo X, Yang T, Cai L, Yeo JH, Yan G et al (2022) Deep learning-based recognition and segmentation of intracranial aneurysms under small sample size. Front Physiol 13:1084202. https://doi.org/10.3389/fphys.2022.1084202
Article PubMed PubMed Central Google Scholar
Giri C, Sharma J, Goodwin M (2022) Brain tumour segmentation on 3D MRI using attention V-Net. In: Iliadis L, Jayne C, Tefas A, Pimenidis E (eds) Engineering applications of neural networks. Springer International Publishing, Cham, pp 336–348
Milletari F, Navab N, Ahmadi SA (2016) V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. IEEE
Yilmaz VS, Akdag M, Dalveren Y, Doruk RO, Kara A, Soylu A (2023) Investigating the impact of two major programming environments on the Accuracy of Deep Learning-based glioma detection from MRI images. Diagnostics (Basel) 13(4). https://doi.org/10.3390/diagnostics13040651
Zhang G, Chen L, Liu A, Pan X, Shu J, Han Y et al (2021) Comparable performance of Deep Learning-based to manual-based Tumor Segmentation in KRAS/NRAS/BRAF Mutation Prediction with MR-Based Radiomics in rectal Cancer. Front Oncol 11:696706. https://doi.org/10.3389/fonc.2021.696706
Article CAS PubMed PubMed Central Google Scholar
Kingma DP, Ba J, Adam (2014) A method for stochastic optimization. CoRR.;abs/1412.6980.
Almajalid R, Zhang M, Shan J (2022) Fully automatic knee bone detection and segmentation on three-dimensional MRI. Diagnostics (Basel) 12(1). https://doi.org/10.3390/diagnostics12010123
He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition. IEEE
Huang G, Liu Z, Maaten LVD, Weinberger KQ Densely Connected Convolutional Networks. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2017. pp. 2261-9
Cheng P, Yang Y, Yu H, He Y (2021) Automatic vertebrae localization and segmentation in CT with a two-stage dense-U-Net. Sci Rep 11(1):22156. https://doi.org/10.1038/s41598-021-01296-1
Article CAS PubMed PubMed Central Google Scholar
Chuang C-H, Lin C-Y, Tsai Y-Y, Lian Z-Y, Xie H-X, Hsu C-C et al (2019) Efficient triple output network for vertebral segmentation and identification. IEEE Access 7:117978–117985
Lessmann N, van Ginneken B, de Jong PA, Išgum I (2019) Iterative fully convolutional neural networks for automatic vertebra segmentation and identification. Med Image Anal 53:142–155. https://doi.org/10.1016/j.media.2019.02.005
Yao J, Burns JE, Forsberg D, Seitel A, Rasoulian A, Abolmaesumi P et al (2016) A multi-center milestone study of clinical vertebral CT segmentation. Comput Med Imaging Graph 49:16–28. https://doi.org/10.1016/j.compmedimag.2015.12.006
Article PubMed PubMed Central Google Scholar
Park T, Yoon MA, Cho YC, Ham SJ, Ko Y, Kim S et al (2022) Automated segmentation of the fractured vertebrae on CT and its applicability in a radiomics model to predict fracture malignancy. Sci Rep 12(1):6735. https://doi.org/10.1038/s41598-022-10807-7
Article CAS PubMed PubMed Central Google Scholar
Kim Y, Kwak G-H (2018) Performance evaluation of machine learning and deep learning algorithms in crop classification. https://doi.org/10.7780/kjrs.2018.34.5.9
Sameen MI, Pradhan B, Aziz OS (2018) Classification of very high resolution aerial photos using spectral-spatial convolutional neural networks. J Sens 2018:7195432. https://doi.org/10.1155/2018/7195432
Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278(2):563–577. https://doi.org/10.1148/radiol.2015151169
Menze BH, Jakab A, Bauer S, Kalpathy-Cramer J, Farahani K, Kirby J et al (2015) The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE Trans Med Imaging 34(10):1993–2024. https://doi.org/10.1109/tmi.2014.2377694
Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y et al (2017) Brain tumor segmentation with deep neural networks. Med Image Anal 35:18–31.
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