Deep ContourFlow: Advancing Active Contours with Deep Learning
This addresses the challenge of limited annotations in histology, offering a robust solution for medical image analysis, though it is incremental as it builds on existing paradigms.
The paper tackles the problem of image segmentation in histology by combining unsupervised active contour models with deep learning, achieving significant improvements over state-of-the-art methods without requiring extensive labeled data.
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.