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.
@article{arxiv.2407.10696,
title = {Deep ContourFlow: Advancing Active Contours with Deep Learning},
author = {Antoine Habis and Vannary Meas-Yedid and Elsa Angelini and Jean-Christophe Olivo-Marin},
journal= {arXiv preprint arXiv:2407.10696},
year = {2024}
}