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Cell instance segmentation in fluorescence microscopy images is becomingessential for cancer dynamics and prognosis. Data extracted from cancerdynamics allows to understand and accurately model different metabolicprocesses such as proliferation. This enables customized and more precisecancer treatments. However, accurate cell instance segmentation, necessary forfurther cell tracking and behavior analysis, is still challenging in scenarioswith high cell concentration and overlapping edges. Within this framework, wepropose a novel cell instance segmentation approach based on the well-knownU-Net architecture. To enforce the learning of morphological information perpixel, a deep distance transformer (DDT) acts as a back-bone model. The DDToutput is subsequently used to train a top-model. The following top-models areconsidered: a three-class (\emph{e.g.,} foreground, background and cell border)U-net, and a watershed transform. The obtained results suggest a performanceboost over traditional U-Net architectures. This opens an interesting researchline around the idea of injecting morphological information into a fullyconvolutional model.

Enforcing Morphological Information in Fully Convolutional Networks to Improve Cell Instance Segmentation in Fluorescence Microscopy Images