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.

翻译：本文提出一种将无监督主动轮廓模型与深度学习相结合的新方法，用于实现鲁棒且自适应的图像分割。传统主动轮廓模型为轮廓演化提供了灵活框架，而深度学习则具备从原始数据中直接学习复杂特征与模式的能力。我们提出的方法融合了两种范式的优势，构建了适用于无监督和单样本学习的图像分割框架。该方法能够捕捉复杂的物体边界，且无需大量标注训练数据。这一特性在组织病理学领域尤为重要，该领域由于标注过程复杂耗时，正面临严重的标注数据短缺问题。我们在组织病理学数据集上对本文方法进行了验证，并与现有先进方法进行了对比，结果表明本方法取得了显著改进。