Deep ContourFlow: Advancing Active Contours with Deep Learning
<|reference_start|>Deep ContourFlow: Advancing Active Contours with Deep Learning: 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.<|reference_end|>
@article{habis2024deep,
    title={Deep ContourFlow: Advancing Active Contours with Deep Learning},
    author={Antoine Habis, Vannary Meas-Yedid, Elsa Angelini, Jean-Christophe
  Olivo-Marin},
    journal={arXiv preprint arXiv:2407.10696},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.10696},
    primaryClass={cs.CV}
}