  This paper introduces a novel approach that combines unsupervised active
contour models with deep learning for robust and adaptive image segmentation.
Indeed, traditional active contours, provide a flexible framework for contour
evolution and learning offers the capacity to learn intricate features and
patterns directly from raw data. Our proposed methodology leverages the
strengths of both paradigms, presenting a framework for both unsupervised and
one-shot approaches for image segmentation. It is capable of capturing complex
object boundaries without the need for extensive labeled training data. This is
particularly required in histology, a field facing a significant shortage of
annotations due to the challenging and time-consuming nature of the annotation
process. We illustrate and compare our results to state of the art methods on a
histology dataset and show significant improvements.
