% 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”.

@INPROCEEDINGS{Conjeti:145576,
      author       = {Conjeti, Sailesh},
      title        = {{D}eep learning for volumetric segmentation in
                      spatio-temporal data: {A}pplication to segmentation of
                      prostate in {DCE}-{MRI}},
      reportid     = {DZNE-2020-00909},
      year         = {2019},
      abstract     = {Segmentation of the prostate in MR images is an essential
                      step that underpins the success of subsequent analysis
                      methods, such as cancer lesion detection inside the tumour
                      and registration between different modalities. This work
                      focuses on leveraging deep learning for analysis of
                      longitudinal volumetric datasets, particularly for the task
                      of segmentation, and presents proof-of-concept for
                      segmentation of the prostate in 3D+T DCE-MRI sequences. A
                      two-stream processing pipeline is proposed for this task,
                      comprising a spatial stream modelled using a volumetric
                      fully convolutional network and a temporal stream modeled
                      using recurrent neural networks with Long-Short-term Memory
                      (LSTM) units. The predictions of the two streams are fused
                      using deep neural networks. The proposed method has been
                      validated on a public benchmark dataset of 17 patients, each
                      with 40 temporal volumes. When averaged over three
                      experiments, a highly competitive Dice overlap score of
                      0.8688 and sensitivity of 0.8694 were achieved. As a
                      spatiotemporal segmentation method, it can easily migrate to
                      other datasets.},
      month         = {Apr},
      date          = {2019-04-08},
      organization  = {IEEE 16th International Symposium on
                       Biomedical Imaging (ISBI 2019), Venice
                       (Italy), 8 Apr 2019 - 11 Apr 2019},
      subtyp        = {Other},
      cin          = {AG Reuter},
      cid          = {I:(DE-2719)1040310},
      pnm          = {345 - Population Studies and Genetics (POF3-345)},
      pid          = {G:(DE-HGF)POF3-345},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://pub.dzne.de/record/145576},
}