Jukka Hirvasniemi
Jukka Hirvasniemi
Erasmus MC
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In vivo comparison of delayed gadolinium-enhanced MRI of cartilage and delayed quantitative CT arthrography in imaging of articular cartilage
J Hirvasniemi, KAM Kulmala, E Lammentausta, R Ojala, P Lehenkari, ...
Osteoarthritis and cartilage 21 (3), 434-442, 2013
Association between quantitative MRI and ICRS arthroscopic grading of articular cartilage
V Casula, J Hirvasniemi, P Lehenkari, R Ojala, M Haapea, S Saarakkala, ...
Knee Surgery, Sports Traumatology, Arthroscopy 24 (6), 2046-2054, 2016
Quantification of differences in bone texture from plain radiographs in knees with and without osteoarthritis
J Hirvasniemi, J Thevenot, V Immonen, T Liikavainio, P Pulkkinen, ...
Osteoarthritis and cartilage 22 (10), 1724-1731, 2014
Ultrasound arthroscopy of human knee cartilage and subchondral bone in vivo
J Liukkonen, P Lehenkari, J Hirvasniemi, A Joukainen, T Virén, ...
Ultrasound in medicine & biology 40 (9), 2039-2047, 2014
Assessment of risk of femoral neck fracture with radiographic texture parameters: a retrospective study
J Thevenot, J Hirvasniemi, P Pulkkinen, M Määttä, R Korpelainen, ...
Radiology 272 (1), 184-191, 2014
Arthroscopic ultrasound technique for simultaneous quantitative assessment of articular cartilage and subchondral bone: an in vitro and in vivo feasibility study
J Liukkonen, J Hirvasniemi, A Joukainen, P Penttilä, T Virén, ...
Ultrasound in medicine & biology 39 (8), 1460-1468, 2013
Correlation of subchondral bone density and structure from plain radiographs with micro computed tomography ex vivo
J Hirvasniemi, J Thevenot, HT Kokkonen, MA Finnilä, MS Venäläinen, ...
Annals of biomedical engineering 44 (5), 1698-1709, 2016
Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and Cohort Knee (CHECK) study
J Hirvasniemi, WP Gielis, S Arbabi, R Agricola, WE van Spil, V Arbabi, ...
Osteoarthritis and Cartilage 27 (6), 906-914, 2019
Adaptive segmentation of knee radiographs for selecting the optimal ROI in texture analysis
N Bayramoglu, A Tiulpin, J Hirvasniemi, MT Nieminen, S Saarakkala
Osteoarthritis and cartilage 28 (7), 941-952, 2020
Differences in tibial subchondral bone structure evaluated using plain radiographs between knees with and without cartilage damage or bone marrow lesions-the Oulu Knee …
J Hirvasniemi, J Thevenot, A Guermazi, J Podlipská, FW Roemer, ...
European radiology 27 (11), 4874-4882, 2017
Trabecular homogeneity index derived from plain radiograph to evaluate bone quality
J Thevenot, J Hirvasniemi, M Finnilä, P Pulkkinen, V Kuhn, T Link, ...
Journal of bone and mineral research 28 (12), 2584-2591, 2013
Bone density and texture from minimally post-processed knee radiographs in subjects with knee osteoarthritis
J Hirvasniemi, J Niinimäki, J Thevenot, S Saarakkala
Annals of biomedical engineering 47 (5), 1181-1190, 2019
Association between radiography-based subchondral bone structure and MRI-based cartilage composition in postmenopausal women with mild osteoarthritis
J Hirvasniemi, J Thevenot, J Multanen, M Haapea, A Heinonen, ...
Osteoarthritis and cartilage 25 (12), 2039-2046, 2017
Rapid CT-based estimation of articular cartilage biomechanics in the knee joint without cartilage segmentation
A Mohammadi, KAH Myller, P Tanska, J Hirvasniemi, S Saarakkala, ...
Annals of biomedical engineering 48 (12), 2965-2975, 2020
Structural risk factors for low-energy acetabular fractures
RK Gebre, J Hirvasniemi, I Lantto, S Saarakkala, J Leppilahti, T Jämsä
Bone 127, 334-342, 2019
Agricola R, van Spil WE, Arbabi V, et al. Bone texture analysis for prediction of incident radiographic hip osteoarthritis using machine learning: data from the Cohort Hip and …
J Hirvasniemi, WP Gielis
Osteoarthr Cartil 27 (6), 906-14, 2019
A machine learning approach to distinguish between knees without and with osteoarthritis using MRI-based radiomic features from tibial bone
J Hirvasniemi, S Klein, S Bierma-Zeinstra, MW Vernooij, D Schiphof, ...
European radiology 31 (11), 8513-8521, 2021
Comparison of bone texture between normal individuals and patients with Kashin-Beck disease from plain radiographs in knee
W Li, J Hirvasniemi, X Guo, S Saarakkala, MJ Lammi, C Qu
Scientific reports 8 (1), 1-9, 2018
The effect of preprocessing on convolutional neural networks for medical image segmentation
KB De Raad, KA van Garderen, M Smits, SR van der Voort, F Incekara, ...
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), 655-658, 2021
Discrimination of low-energy acetabular fractures from controls using computed tomography-based bone characteristics
RK Gebre, J Hirvasniemi, I Lantto, S Saarakkala, J Leppilahti, T Jämsä
Annals of biomedical engineering 49 (1), 367-381, 2021
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