Label merge-and-split: A graph-colouring approach for memory-efficient
  brain parcellation
Aaron Kujawa Reuben Dorent Sebastien Ourselin Tom Vercauteren
Whole brain parcellation requires inferring hundreds of segmentation labels
in large image volumes and thus presents significant practical challenges for
deep learning approaches. We introduce label merge-and-split, a method that
first greatly reduces the effective number of labels required for
learning-based whole brain parcellation and then recovers original labels.
Using a greedy graph colouring algorithm, our method automatically groups and
merges multiple spatially separate labels prior to model training and
inference. The merged labels may be semantically unrelated. A deep learning
model is trained to predict merged labels. At inference time, original labels
are restored using atlas-based influence regions. In our experiments, the
proposed approach reduces the number of labels by up to 68% while achieving
segmentation accuracy comparable to the baseline method without label merging
and splitting. Moreover, model training and inference times as well as GPU
memory requirements were reduced significantly. The proposed method can be
applied to all semantic segmentation tasks with a large number of spatially
separate classes within an atlas-based prior.