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Journal Article

  1. [MART-21]“Automatic Segmentation and Location Learning of Neonatal Cerebral Ventricles in 3D Ultrasound Data Combining CNN and CPPN”, Computers in Biology and Medicine, 2021.PDF icon Martin-21_automatic segmentation and location learning of neonatal cerebral ventricles in 3D cUS.pdf (4.07 MB)
  2. [GROS-18]“Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.”, Med Image Anal, vol. 44, pp. 215-227, 2018.PDF icon gros2018-optiC.pdf (3.74 MB)
  3. [sdika2018]“Repetitive Motion Compensation for Real Time Intraoperative Video Processing”, Medical Image Analysis, 2018.PDF icon sdika2018_cmp.pdf (14.23 MB)
  4. [NEME-17g]“Reproducibility of in vivo Magnetic Resonance Imaging T1rho and T2 relaxation time measurements of hip cartilage at 3.0T in healthy volunteers”, JMRI, 2017.
  5. [SDIKA-15a]M. Sdika, Tonson, A., Le Fur, Y., Cozzone, P. J., and Bendahan, D., “Multi-atlas-based fully automatic segmentation of individual muscles in rat leg.”, Magma (New York, N.Y.), 2015.
  6. [TASO-15]M. Taso, Le Troter, A., Sdika, M., Cohen-Adad, J., Arnoux, P. J., Guye, M., Ranjeva, J. P., and Callot, V., “A reliable spatially normalized template of the human spinal cord - Applications to automated white matter/gray matter segmentation and tensor-based morphometry (TBM) mapping of gray matter alterations occurring with age.”, NeuroImage, vol. 117, pp. 20-8, 2015.
  7. [VANR-15]“MisoSR: Medical Image Isotropic Super-Resolution Reconstruction”, Midas Journal, 2015.
  8. [SDIK-15b]M. Sdika, “Enhancing atlas based segmentation with multiclass linear classifiers.”, Medical physics, vol. 42, no. 12, p. 7169, 2015.PDF icon sdika2015-atlas-linear-classif.pdf (659.11 KB)
  9. [MERH-14]D. Merhej, Ratiney, H., Diab, C., Khalil, M., Sdika, M., Prost, R., and , “Fast multidimensional NMR spectroscopy for sparse spectra.”, NMR Biomed, vol. 27, no. 6, pp. 640-55, 2014.
  10. [FONO-14]V. S. Fonov, Le Troter, A., Taso, M., De Leener, B., Lévêque, G., Benhamou, M., Sdika, M., Benali, H., Pradat, P. F., Collins, D. L., Callot, V., and Cohen-Adad, J., “Framework for integrated MRI average of the spinal cord white and gray matter: The MNI-Poly-AMU template.”, NeuroImage, vol. 102 Pt 2, pp. 817-27, 2014.
  11. [TASO-13]M. Taso, Le Troter, A., Sdika, M., Ranjeva, J., Guye, M., Bernard, M., and Callot, V., “Construction of an in vivo human spinal cord atlas based on high-resolution MR images at cervical and thoracic levels: preliminary results.”, Magma, 2013.
  12. [GOUR-13]“A genome-wide association study of brain lesion distribution in multiple sclerosis.”, Brain, vol. 136, no. Pt 4, pp. 1012-24, 2013.
  13. [SDIK-13]M. Sdika, “A Sharp Sufficient Condition for B-Spline Vector Field Invertibility. Application to Diffeomorphic Registration and Interslice Interpolation.”, SIAM Journal on Imaging Sciences, vol. 6, pp. 2236-2257, 2013.PDF icon sdika-2013-bspline-invertibility-condition.pdf (747.84 KB)
    http://epubs.siam.org/doi/abs/10.1137/120879920
  14. [CHUN-10]S. Chung, Courcot, B., Sdika, M., Moffat, K., Rae, C., and Henry, R. G., “Bootstrap quantification of cardiac pulsation artifact in DTI.”, Neuroimage, vol. 49, no. 1, pp. 631-40, 2010.
  15. [SDIK-10]“Combining atlas based segmentation and intensity classification with nearest neighbor transform and accuracy weighted vote.”, Med Image Anal, vol. 14, no. 2, pp. 219-26, 2010.PDF icon sdika2010-accuracy-vote-preprint.pdf (426.79 KB)
  16. [SDIK-09]M. Sdika and Pelletier, D., “Nonrigid registration of multiple sclerosis brain images using lesion inpainting for morphometry or lesion mapping.”, Hum Brain Mapp, vol. 30, no. 4, pp. 1060-7, 2009.PDF icon sdika2009-nrigid-lesions-preprint.pdf (240.81 KB)
  17. [CHUN-08]S. Chung, Pelletier, D., Sdika, M., Lu, Y., Berman, I., and Henry, G., “Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing.”, NeuroImage, vol. 39, no. 4, pp. 1693-705, 2008.
  18. [SDIK-08]M. Sdika, “A fast nonrigid image registration with constraints on the Jacobian using large scale constrained optimization.”, IEEE Trans Med Imaging, vol. 27, no. 2, pp. 271-81, 2008.PDF icon sdika2008tmi.fast-nrigid-jacobian.pdf (1.17 MB)
  19. [RATI-07a]H. Ratiney, Noworolski, S. M., Sdika, M., Srinivasan, R., Henry, R. G., Nelson, S. J., and Pelletier, D., “Estimation of metabolite T1 relaxation times using tissue specific analysis, signal averaging and bootstrapping from magnetic resonance spectroscopic imaging data”, Magn Reson Mater Phy Biol Med, vol. 20, pp. 143-155, 2007.
  20. [TRIG-06]B. Triggs and Sdika, M., “Boundary conditions for Young-van Vliet recursive filtering”, Signal Processing, IEEE Transactions on, vol. 54, pp. 2365–2367, 2006.
  21. [RATI-05]H. Ratiney, Sdika, M., Coenradie, Y., Cavassila, S., van Ormondt, D., and Graveron-Demilly, D., “Time-Domain Semi-Parametric Estimation Based on a Metabolite Basis Set”, Nuclear Magnetic Resonance in Biomedicine, vol. 18, pp. 1-13, 2005.

Conference Paper

  1. [MART-19]“Training database elaboration strategies for preterm neonate cerebral ventricular system segmentation with 2D convolutional neural network in 3D US data”, in IEEE International Ultrasonics Symposium, 2019.PDF icon Martin_IUS_2019.pdf (974.69 KB)
  2. [CARE-18a]“Neuroimagerie fonctionnelle multispectrale”, in OPTDIAG, Paris, 2018.
  3. [GROS-17a]“OptiC: Robust and Automatic Spinal Cord Localization on a Large Variety of MRI Data Using a Distance Transform Based Global Optimization”, in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2017, pp. 712–719.
  4. [SDIK-17]“Automatic Detection of the Spinal Cord Centerline using Machine Learning and Global Nonlinear Optimization”, in Recherche en Imagerie et Technologies pour la Santé (RITS), 2017.PDF icon sdika-rits2017.pdf (2.44 MB)
  5. [SDIK-16]“Robust real time motion compensation for intraoperative video processing during neurosurgery”, in Biomedical Imaging (ISBI), 2016 IEEE 13th International Symposium on, 2016, pp. 1046–1049.PDF icon sdika_isbi2016_cmp.pdf (1.01 MB)
  6. [NEME-16e]“Repeatability and Reproducibility of in Vivo Magnetic Resonance T1rho Relaxation Time Measurements of Hip Cartilage at 3T”, in 24th Annual meeting of the ISMRM, Singapore, 2016.
  7. [NEME-16a]“Repeatability and reproducibility of in vivo magnetic resonance T1rho relaxation time measurements of hip cartilage at 3T”, in ISMRM, Singapore, 2016.
  8. [VANR-15c]E. Van Reeth, Sdika, M., Gaillard, S., Luppi, P. - H., Libourel, P. - A., and Beuf, O., “Improving the spatial resolution and SNR of rat brain T2-weighted MR images: application of a super-resolution method”, in ISMRM, Toronto, 2015.
  9. [VANR-15a]E. Van Reeth, Sdika, M., Gaillard, S., Luppi, P. - H., Libourel, P. - A., and Beuf, O., “Amélioration de résolution spatiale et SNR d’images cérébrales pondérées T2 de rat : application de la super-résolution”, in SFRMBM, Grenoble, France, 2015.
  10. [KECH-14]R. Kéchichian, Valette, S., Sdika, M., and Desvignes, M., “Automatic 3D Multiorgan Segmentation via Clustering and Graph Cut Using Spatial Relations and Hierarchically-Registered Atlases”, in Medical Computer Vision: Algorithms for Big Data, 2014, pp. 201-209.
  11. [TASO-14a]M. Taso, Le Troter, A., Sdika, M., Fonov, V., Cohen-Adad, J., Guye, M., Ranjeva, J., and Callot, V., “Validation of a 2D Spinal Cord Probabilistic Atlas. Application to FA Measurement and VBM Study of the GM Atrophy Occurring with Age”, in ISMRM 2014, 2014.
  12. [FONO-14]V. Fonov, Le Troter, A., Taso, M., Leveque, G., Benhamou, M., Sdika, M., Collins, L., Callot, V., and Cohen-Adad, J., “MNI-Poly-AMU Average Anatomical Template for Automatic Spinal Cord Measurements”, in ISMRM 2014, 2014.
  13. [BENH-14]M. Benhamou, Fonov, V., Taso, M., Le Troter, A., Sdika, M., Collins, L., Callot, V., and Cohen-Adad, J., “Atlas of White-Matter Tracts in the Human Spinal Cord”, in ISMRM 2014, 2014.
  14. [FRIN-13a]C. Frindel, Rositi, H., Sdika, M., Chauveau, F., Cho, T. H., Neyran, B., Rousseau, D., Wiart, M., Berthezène, Y., and Nighoghossian, N., “Démonstrateur en imagerie à vocation diagnostique dans le cadre de l’AVC”, in 4ème Journée Démonstrateurs, Angers, France, 2013.
  15. [SDIK-11a]M. Sdika, Tonson, A., Cozzone, P., and Bendahan, D., “Multi Atlas Segmentation of Rat Leg Muscles”, in 20th Annual meeting of the ISMRM, Montreal, Canada, 2011, p. 2562.
  16. [DUHA-10]G. Duhamel, Marqueste, T., Sdika, M., Tachrount, M., Decherchi, P., Cozzone, P., and Callot, V., “Vascular Alterations and Recruitment in Spinal Cord Injury Revealed by Multislice Arterial Spin Labeling (ASL) Perfusion Imaging”, in ISMRM-ESMRMB Joint Annual Meeting, Stockholm, Sweden, 2011, p. 5092.
  17. [SDIK-11b]M. Sdika, Le Fur, Y., and Cozzone, P., “Optimal Recombination of Multi-Coils CSI Data using Image Based Sensitivity Map”, in 20th Annual meeting of the ISMRM, Montreal, Canada, 2011, p. 3478.
  18. [RATI-11]H. Ratiney, Sdika, M., Beuf, O., Le Fur, Y., and Cavassila, S., “Towards a biochemical and structure specific quantitative analysis of the mouse brain using Magnetic Resonance Spectroscopic Imaging at 11.7T”, in Journées Thématiques GDR « Imagerie in vivo » (IMAGIV), Paris, 2011.
  19. [SDIK-10b]M. Sdika, Callot, V., Hebert, M., Duhamel, G., and Cozzone, P. J., “Segmentation of the Structure of the Mouse Spinal Cord on DTI images”, in ISMRM-ESMRMB Joint Annual Meeting, Stockholm,Sweden, 2010, p. 5115.
  20. [SDIK-10c]M. Sdika, Callot, V., Hebert, M., Duhamel, G., and Cozzone, P. J., “A Fully Automated White Matter/Gray Matter Segmentation of Mice Spinal Cord on DTI Images”, in ISMRM-ESMRMB Joint Annual Meeting, Stockholm,Sweden, 2010, p. 442.
  21. [RATI-10a]H. Ratiney, Le Fur, Y., Sdika, M., and Cavassila, S., “Short Echo Time H1 Chemical Shift Imaging data quantification in the mouse brain at 11.7T using a constrained parametric macromolecular model”, in ISMRM-ESMRMB Joint Annual Meeting, Stockholm,Sweden, 2010.
  22. [RATI-09]H. Ratiney, Sdika, M., Beuf, O., Le Fur, Y., and Cavassila, S., “Towards structure specific macromolecular content of mouse brain from Chemical Shift Imaging data at 11.7T”, in ISMRM, Hawai, USA, 2009, p. 3293.
  23. [RATI-09a]H. Ratiney, Le Fur, Y., Sdika, M., Touret, M., Bernard, A., and Cavassila, S., “Quantitative assessment of the brain macromolecular content in a mouse model of neuro-inflammation from chemical shift imaging data at 11.7T”, in ESMRMB, Antalya,Turkey, 2009, p. 39.
  24. [RATI-08c]H. Ratiney, Bucur, A., Sdika, M., Beuf, O., Pilleul, F., and Cavassila, S., “Effective Voigt model estimation using multiple random starting values and parameter bounds settings for in vivo hepatic 1H Magnetic Resonance Spectroscopy data”, in IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, 2008, pp. 1529-1532.
  25. [RATI-06b]H. Ratiney, Noworolski, S. M., Sdika, M., Srinivasan, R., Henry, R. G., Nelson, S. J., and Pelletier, D., “Regional Estimation of T1 Metabolite Relaxation using 2D MRSI and a Bootstrap Approach at 1.5T”, in Int. Soc. Magnetic Resonance in Medicine, 14th Scientific Meeting and Exhibition, Seattle, Washington, USA, 2006.
  26. [DOUZ-05]M. Douze, Sdika, M., and Schmid, C., “PhotoMole: retrieval from a database of natural images”, in Demo at the International Conference on Computer Vision, 2005.
  27. [RATI-05d]H. Ratiney, Capobianco, E., Sdika, M., Rabeson, H., Cudalbu, C., Cavassila, S., de Beer, R., van Ormondt, D., and Graveron-Demilly, D., “Semi-Parametric estimation in In vivo MR Spectroscopy”, in ProRISC, IEEE Benelux, Veldhoven, The Netherlands, 2005, pp. 658-667.

Conference Proceedings

  1. [OGIE-17]“Individual muscle segmentation in MR images: A 3D propagation through 2D non-linear registration approaches.”, Conf Proc IEEE Eng Med Biol Soc, vol. 2017. pp. 317-320, 2017.
  2. [VANR-15b]E. Van Reeth, Sdika, M., Luppi, P. - H., Libourel, P. - A., and Beuf, O., “Multiple Labels Point-Set Registration”, IEEE International Symposium on Biomedical Imaging. New York, 2015.PDF icon isbi_finalVersion.pdf (762.65 KB)
  3. [RATI-08a]H. Ratiney, Bucur, A., Sdika, M., Beuf, O., Pilleul, F., and Cavassila, S., “Effective Voigt model estimation using multiple random starting values and parameter bounds settings for in vivo hepatic 1H Magnetic Resonance Spectroscopy data.”, 5th IEEE International Symposium on Biomedical Imaging: From nano to macro . pp. 1529-1532, 2008.
    http://dx.doi.org/10.1109/ISBI.2008.4541300