% 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”.

@MASTERSTHESIS{Druska:1022001,
      author       = {Druska, Oskar and Kadelka, Tobias},
      othercontributors = {Waite, Alexander and Prof. Dr. rer. nat. Grajewski,
                          Matthias},
      title        = {{M}odifying an existing {C}onvolutional {N}eural {N}etwork
                      to predict {T}otal {I}ntracranial {V}olume using {T}1w
                      images},
      school       = {FH Aachen University of Applied Sciences},
      type         = {Course work},
      reportid     = {FZJ-2024-01138},
      pages        = {27 pages},
      year         = {2023},
      note         = {Course work, FH Aachen University of Applied Sciences,
                      2023},
      abstract     = {Neuroimaging has become an essential part in diagnosing and
                      treating neurological diseases.Statistics such as sex, age,
                      and dexterity can be asked for by medical staff during
                      consultation. Biological markers or medical information such
                      as total intracranial volume (colloq.: brain volume; TIV) or
                      absolute and relative amount of grey/white matter have to be
                      computed by usually timeconsuming calculations.It is of both
                      patient’s and medical staff’s interest to have results
                      as soon as possible after collecting data from magnetic
                      resonsance imaging (MRI), a type of imaging
                      procedure.Machine Learning provides statistical tools to
                      omit classical time-consuming processing and make
                      predictions about those biological markers based on
                      previously analyzed brain images. This allows faster
                      estimates and enables timely discussion and diagnosis with
                      and of the patient after examination.Especially in emergency
                      situations this can provide doctors with required
                      information and allows them to start treatment while the
                      patient is still on-site, if necessary.There have been
                      previous efforts on building a Convolutional Neural Network
                      (CNN) that is able to predict brainage on a given
                      T1-weighted image. T1w images are a type of neuroimaging
                      data.This thesis will describe modifications done to the
                      given CNN ’Simple Fully Convolutional Network’ (SFCN),
                      in order to predict TIV using the same T1w image of the
                      brain as input.It will furhter explain various Machine
                      Learning concepts and the SFCN’s implementation as well as
                      the training and evaluation functions used as the foundation
                      for this appliance.This modification is done by linear
                      scaling the given TIV values used for back- propagation into
                      the space the SFCN has previously been trained and applied
                      on, in this case the age range of [42, 82]. This has been
                      implemented into the code of SFCN.After modifying, the model
                      has been trained for 10 epochs using 1370 T1w brain images
                      provided by the Amsterdam Open MRI Collection (AOMIC)Using
                      the Kullback-Leibler divergence as a measure of loss, the
                      model shows a training loss of 2.46 after 1 epoch and 1.98
                      after 10 epochs.Its validation loss initially starts at 3.4
                      while ending at 3.45 after 10 epochs. The validation loss is
                      minimal after 2 epochs with a KL-divergence of 2.7.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)9},
      doi          = {10.34734/FZJ-2024-01138},
      url          = {https://juser.fz-juelich.de/record/1022001},
}