Can a single image processing algorithm work equally well across all
  phases of DCE-MRI?
Adam G. Tattersall Keith A. Goatman Lucy E. Kershaw Scott I. K. Semple Sonia Dahdouh
Image segmentation and registration are said to be challenging when applied
to dynamic contrast enhanced MRI sequences (DCE-MRI). The contrast agent causes
rapid changes in intensity in the region of interest and elsewhere, which can
lead to false positive predictions for segmentation tasks and confound the
image registration similarity metric. While it is widely assumed that contrast
changes increase the difficulty of these tasks, to our knowledge no work has
quantified these effects. In this paper we examine the effect of training with
different ratios of contrast enhanced (CE) data on two popular tasks:
segmentation with nnU-Net and Mask R-CNN and registration using VoxelMorph and
VTN. We experimented further by strategically using the available datasets
through pretraining and fine tuning with different splits of data. We found
that to create a generalisable model, pretraining with CE data and fine tuning
with non-CE data gave the best result. This interesting find could be expanded
to other deep learning based image processing tasks with DCE-MRI and provide
significant improvements to the models performance.