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Instance segmentation is a key step for quantitative microscopy. Whileseveral machine learning based methods have been proposed for this problem,most of them rely on computationally complex models that are trained onsurrogate tasks. Building on recent developments towards end-to-end trainableinstance segmentation, we propose a minimalist recurrent network calledrecurrent dilated convolutional network (RDCNet), consisting of a sharedstacked dilated convolution (sSDC) layer that iteratively refines its outputand thereby generates interpretable intermediate predictions. It islight-weight and has few critical hyperparameters, which can be related tophysical aspects such as object size or density.We perform a sensitivityanalysis of its main parameters and we demonstrate its versatility on 3 taskswith different imaging modalities: nuclear segmentation of H&E slides, of 3Danisotropic stacks from light-sheet fluorescence microscopy and leafsegmentation of top-view images of plants. It achieves state-of-the-art on 2 ofthe 3 datasets.

RDCNet: Instance segmentation with a minimalist recurrent residual network