Deep ContourFlow: Advancing Active Contours with Deep Learning
- URL: http://arxiv.org/abs/2407.10696v1
- Date: Mon, 15 Jul 2024 13:12:34 GMT
- Title: Deep ContourFlow: Advancing Active Contours with Deep Learning
- Authors: Antoine Habis, Vannary Meas-Yedid, Elsa Angelini, Jean-Christophe Olivo-Marin,
- Abstract summary: We present 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.
- Score: 3.9948520633731026
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
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