Deep ContourFlow: Advancing Active Contours with Deep Learning

TL;DR

Deep ContourFlow combines unsupervised active contour models with deep learning to enable robust, adaptive, and annotation-efficient image segmentation, especially useful in histology where labeled data is scarce.

Contribution

It introduces a novel framework that integrates active contours with deep learning for unsupervised and one-shot image segmentation, addressing annotation scarcity.

Findings

Achieves significant improvements over state-of-the-art methods on histology data.

Capable of capturing complex object boundaries without extensive labeled data.

Demonstrates robustness and adaptability in challenging segmentation tasks.

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.