% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Corona:144980,
      author       = {Corona, Veronica and Lellmann, Jan and Nestor, Peter and
                      Schönlieb, Carola-Bibiane and Acosta-Cabronero, Julio},
      title        = {{A} multi-contrast {MRI} approach to thalamus
                      segmentation.},
      journal      = {Human brain mapping},
      volume       = {41},
      number       = {8},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {DZNE-2020-00344},
      pages        = {2104-2120},
      year         = {2020},
      abstract     = {Thalamic alterations occur in many neurological disorders
                      including Alzheimer's disease, Parkinson's disease and
                      multiple sclerosis. Routine interventions to improve symptom
                      severity in movement disorders, for example, often consist
                      of surgery or deep brain stimulation to diencephalic nuclei.
                      Therefore, accurate delineation of grey matter thalamic
                      subregions is of the upmost clinical importance. MRI is
                      highly appropriate for structural segmentation as it
                      provides different views of the anatomy from a single
                      scanning session. Though with several contrasts potentially
                      available, it is also of increasing importance to develop
                      new image segmentation techniques that can operate
                      multi-spectrally. We hereby propose a new segmentation
                      method for use with multi-modality data, which we evaluated
                      for automated segmentation of major thalamic subnuclear
                      groups using T1 -weighted, T 2 * -weighted and quantitative
                      susceptibility mapping (QSM) information. The proposed
                      method consists of four steps: Highly iterative image
                      co-registration, manual segmentation on the average
                      training-data template, supervised learning for pattern
                      recognition, and a final convex optimisation step imposing
                      further spatial constraints to refine the solution. This led
                      to solutions in greater agreement with manual segmentation
                      than the standard Morel atlas based approach. Furthermore,
                      we show that the multi-contrast approach boosts segmentation
                      performances. We then investigated whether prior knowledge
                      using the training-template contours could further improve
                      convex segmentation accuracy and robustness, which led to
                      highly precise multi-contrast segmentations in single
                      subjects. This approach can be extended to most 3D imaging
                      data types and any region of interest discernible in single
                      scans or multi-subject templates.},
      keywords     = {Adult / Gray Matter: anatomy $\&$ histology / Gray Matter:
                      diagnostic imaging / Humans / Image Processing,
                      Computer-Assisted / Magnetic Resonance Imaging: methods /
                      Neuroimaging: methods / Pattern Recognition, Automated /
                      Supervised Machine Learning / Thalamic Nuclei: anatomy $\&$
                      histology / Thalamic Nuclei: diagnostic imaging},
      cin          = {AG Nestor},
      ddc          = {610},
      cid          = {I:(DE-2719)1310001},
      pnm          = {344 - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31957926},
      pmc          = {pmc:PMC7267924},
      doi          = {10.1002/hbm.24933},
      url          = {https://pub.dzne.de/record/144980},
}