Few-shot adaptation for morphology-independent cell instance
  segmentation
Ram J. Zaveri Voke Brume Gianfranco Doretto
Microscopy data collections are becoming larger and more frequent. Accurate
and precise quantitative analysis tools like cell instance segmentation are
necessary to benefit from them. This is challenging due to the variability in
the data, which requires retraining the segmentation model to maintain high
accuracy on new collections. This is needed especially for segmenting cells
with elongated and non-convex morphology like bacteria. We propose to reduce
the amount of annotation and computing power needed for retraining the model by
introducing a few-shot domain adaptation approach that requires annotating only
one to five cells of the new data to process and that quickly adapts the model
to maintain high accuracy. Our results show a significant boost in accuracy
after adaptation to very challenging bacteria datasets.