  Dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) is a widely
used multi-phase technique routinely used in clinical practice. DCE and similar
datasets of dynamic medical data tend to contain redundant information on the
spatial and temporal components that may not be relevant for detection of the
object of interest and result in unnecessarily complex computer models with
long training times that may also under-perform at test time due to the
abundance of noisy heterogeneous data. This work attempts to increase the
training efficacy and performance of deep networks by determining redundant
information in the spatial and spectral components and show that the
performance of segmentation accuracy can be maintained and potentially
improved. Reported experiments include the evaluation of training/testing
efficacy on a heterogeneous dataset composed of abdominal images of pediatric
DCE patients, showing that drastic data reduction (higher than 80%) can
preserve the dynamic information and performance of the segmentation model,
while effectively suppressing noise and unwanted portion of the images.
