Unsupervised Decomposition Networks for Bias Field Correction in MR
  Image
Dong Liang Xingyu Qiu Kuanquan Wang Gongning Luo Wei Wang Yashu Liu
Bias field, which is caused by imperfect MR devices or imaged objects,
introduces intensity inhomogeneity into MR images and degrades the performance
of MR image analysis methods. Many retrospective algorithms were developed to
facilitate the bias correction, to which the deep learning-based methods
outperformed. However, in the training phase, the supervised deep
learning-based methods heavily rely on the synthesized bias field. As the
formation of the bias field is extremely complex, it is difficult to mimic the
true physical property of MR images by synthesized data. While bias field
correction and image segmentation are strongly related, the segmentation map is
precisely obtained by decoupling the bias field from the original MR image, and
the bias value is indicated by the segmentation map in reverse. Thus, we
proposed novel unsupervised decomposition networks that are trained only with
biased data to obtain the bias-free MR images. Networks are made up of: a
segmentation part to predict the probability of every pixel belonging to each
class, and an estimation part to calculate the bias field, which are optimized
alternately. Furthermore, loss functions based on the combination of fuzzy
clustering and the multiplicative bias field are also devised. The proposed
loss functions introduce the smoothness of bias field and construct the soft
relationships among different classes under intra-consistency constraints.
Extensive experiments demonstrate that the proposed method can accurately
estimate bias fields and produce better bias correction results. The code is
available on the link:
https://github.com/LeongDong/Bias-Decomposition-Networks.