  Instance segmentation is a key step for quantitative microscopy. While
several machine learning based methods have been proposed for this problem,
most of them rely on computationally complex models that are trained on
surrogate tasks. Building on recent developments towards end-to-end trainable
instance segmentation, we propose a minimalist recurrent network called
recurrent dilated convolutional network (RDCNet), consisting of a shared
stacked dilated convolution (sSDC) layer that iteratively refines its output
and thereby generates interpretable intermediate predictions. It is
light-weight and has few critical hyperparameters, which can be related to
physical aspects such as object size or density.We perform a sensitivity
analysis of its main parameters and we demonstrate its versatility on 3 tasks
with different imaging modalities: nuclear segmentation of H&E slides, of 3D
anisotropic stacks from light-sheet fluorescence microscopy and leaf
segmentation of top-view images of plants. It achieves state-of-the-art on 2 of
the 3 datasets.
