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Analysis of Bone Images

Subprojects:

  • Imaging trabecular bone in vivo with CBCT
    While HRpQCT is already used in clinics, unlike CBCT, it is usually not available in most hospitals. In this subproject we aim at improving the imaging and image processing of CBCT towards its use in clinical routine in osteoporosis.

  • Image processing of trabecular bone in gray scale
    Segmentation of images acquired in vivo of trabecular bone can introduce biases in the estimation of features. The aim of this subproject is to replace the standard methods that are based on binary images to gray scale surrogates.

  • Fabric tensors
    Fabric tensors are mathematical entities that can be used to describe the main orientation and anisotropy of porous media. Standard methods are widely used but they might not be the best choice depending on the application. Our aim is to come up with efficient methods that are better suited to biomechanics estimation.

  • Image processing-based biomechanics
    Biomechanical parameters are usually obtained from FEM simulations on 3D images of trabecular bone. These simulations are expensive and can hinder its use in clinics. The aim of this subproject is to develop new methods for estimating biomechanical parameters through efficient image processing techniques.

  • Clinical validation
    The aim of this subproject is to use our techniques to help answering different clinically relevant questions both in clinical and animal studies.

Publications

 Journal Publications

  • I. Guha, B. Klintström, E. Klintström, X. Zhang, Ö. Smedby,R. Moreno, P. K. Saha, A comparative study of trabecular bonemicro-structural measurements using different ct modalities, Physics inMedicine & Biology 65 (23) (2020) 235029.
  • L. Tanzi, E. Vezzetti, R. Moreno, A. Aprato, A. Audisio, A. Massè,Hierarchical fracture classification of proximal femur x-ray images usinga multistage deep learning approach, European Journal of Radiology133 (2020) 109373.
  • L. Tanzi, E. Vezzetti, R. Moreno, S. Moos, X-ray bone fracture classifi-cation using deep learning: a baseline for designing a reliable approach,Applied Sciences 10 (4) (2020) 1507.
  • E. Klintström, B. Klintström, D. Pahr, T. B. Brismar, Ö. Smedby,R. Moreno, Direct estimation of human trabecular bone stiffness us-ing cone beam computed tomography, Oral surgery, oral medicine, oralpathology and oral radiology 126 (1) (2018) 72–82.
  • E. Klintström, B. Klintström, R. Moreno, T. B. Brismar, D. H. Pahr, Ö. Smedby, Predicting trabecular bone stiffness from clinical cone-beamct and hr-pqct data; an in vitro study using finite element analysis, PloSone 11 (8) (2016) e0161101.
  • R. Moreno, Ö. Smedby, D. H. Pahr, Prediction of apparent trabecularbone stiffness through fourth-order fabric tensors, Biomechanics andmodeling in mechanobiology 15 (4) (2016) 831–844.
  • E. Klintström, Ö. Smedby, B. Klintström, T. Brismar, R. Moreno, Tra-becular bone histomorphometric measurements and contrast-to-noise ra-tio in cbct, Dentomaxillofacial Radiology 43 (8) (2014) 20140196.
  • E. Klintström, Ö. Smedby, R. Moreno, T. B. Brismar, Trabecular bonestructure parameters from 3d image processing of clinical multi-slice andcone-beam computed tomography data, Skeletal radiology 43 (2) (2014)197–204.
  • R. Moreno, M. Borga, Ö. Smedby, Generalizing the mean interceptlength tensor for gray-level images, Medical physics (Lancaster) 39 (7)(2012) 4599–4612.

Conference proceedings

  • B. Klintström, E. Klintström, Ö. Smedby, R. Moreno, Feature spaceclustering for trabecular bone segmentation, in: Scandinavian Confer-ence on Image Analysis, Springer, Cham, 2017, pp. 65–75.
  • M. Chowdhury, B. Klintström, E. Klintström, Ö. Smedby, R. Moreno,Granulometry-based trabecular bone segmentation, in: ScandinavianConference on Image Analysis, Springer, Cham, 2017, pp. 100–108.
  • M. Chowdhury, D. Jörgens, C. Wang, Ö. Smedby, R. Moreno, Segmen-tation of cortical bone using fast level sets, in: Medical Imaging 2017:Image Processing, Vol. 10133, International Society for Optics and Pho-tonics, 2017, p. 1013327.
  • R. Moreno, P. Segers, C. Debbaut, Estimation of the permeability tensorof the microvasculature of the liver through fabric tensors, in: Compu-tational Biomechanics for Medicine, Springer, Cham, 2017, pp. 71–79.
  • R. Moreno, M. Borga, E. Klintström, T. Brismar, Ö. Smedby,Anisotropy estimation of trabecular bone in gray-scale: Comparisonbetween cone beam and micro computed tomography data, in: Develop-ments in Medical Image Processing and Computational Vision, Springer,Cham, 2015, pp. 207–220.
  • R. Moreno, Ö. Smedby, Volume-based fabric tensors through lattice-boltzmann simulations, in: Int. Conf. on Pattern Recognition (ICPR),2014, pp. 3179–3184.
  • R. Moreno, M. Borga, E. Klintström, T. Brismar, Ö. Smedby, Corre-lations between fabric tensors computed on cone beam and microcom-puted tomography images, in: Computational Vision and Medical ImageProcessing, VIPIMAGE 2013, CRC Press, 2013, pp. 393–398.
  • R. Moreno, M. Borga, Ö. Smedby, Evaluation of the plate-rod modelassumption of trabecular bone, in: 2012 9th IEEE International Sym-posium on Biomedical Imaging (ISBI), IEEE, 2012, pp. 470–473.
  • R. Moreno, M. Borga, Ö. Smedby, Estimation of trabecular thickness ingray-scale images through granulometric analysis, in: Medical Imaging2012: Image Processing, Vol. 8314, International Society for Optics andPhotonics, 2012, p. 831451.
  •  R. Moreno, Ö. Smedby, M. Borga, Soft classification of trabeculae intrabecular bone, in: 2011 IEEE International Symposium on BiomedicalImaging: From Nano to Macro, IEEE, 2011, pp. 1641–1644.

Conference abstracts

  • F. Sinzinger, D. Pahr, R. Moreno, Predicting the trabecular bone stiff-ness tensor with spherical convolutional neural networks, in: Congressof the European Society of Biomechanics, 2019.
  • N. Batool, M. Chowdhury, Ö. Smedby, R. Moreno, Estimation of tra-becular bone thickness in gray scale: a validation study, International Journal of Computer Assisted Radiology and Surgery 12 (Supplement1) (2017) S200.
  • M. Plattén, M. Chowdhury, Ö. Smedby, R. Moreno, Estimation of tra-becular thickness in grayscale: an in vivo study, in: European Society ofMusculoskeletal Radiology, Annual Scientific Meeting, 2017, pp. P–0196.
  • E. Klintström, B. Klintström, T. Brismar, Ö. Smedby, R. Moreno, Clin-ical dental cone beam computed tomography - a tool for monitoringtrabecular bone structure?, in: European Congress of Radiology (ECR),no. http://dx.doi.org/10.1594/ecr2015/C-1213, 2015, pp. C–1213.
  • E. Klintström, R. Moreno, T. Brismar, Ö. Smedby, Trabecular bonestructure parameters from cone beam computed tomography data, in:27th Congress of the European Society of Head and Neck Radiology(ESHNR), 2014, pp. 73–74.
  • E. Klintström, R. Moreno, T. Brismar, Ö. Smedby, Three-dimensionalimage processing for measuring trabecular bone structure parameters,in: European Association of Dentomaxillofacial Radiology (EADMFR),Leipzig, Germany, June 13-16, 2012, 2012.

Funding

  • Eurostars 2017-2020 (PI Rodrigo Moreno)

  • Eurostars 2015-2017 (PI Örjan Smedby)

  • Swedish Research Council 2006-2009 (PI Örjan Smedby)

Collaborations

  • Linköping University

  • Technical University Vienna

  • Karolinska Institute

  • SCANCO

  • Imacomp

  • University of Iowa

Thesis projects

Eva Klintström. Image Analysis for Trabecular Bone. Properties on Cone-Beam CT Data, PhD Thesis, Linköping University, 2017.

Contact person

Rodrigo Moreno
Rodrigo Moreno professor rodmore@kth.se +4687909787 Profile
DIVISION OF BIOMEDICAL IMAGING

Contributors

Örjan Smedby
Örjan Smedby professor
Fabian Sinzinger
Fabian Sinzinger fofu-engineer

Former contributors

  • Manish Chowdhury 

  • Nazre Batool 

  • Michael Platten 

  • Jelle van Kerkvoorde 

  • Leonardo Tanzi