A new approach for image segmentation based on diffeomorphic registration and gradient fields

TL;DR

This paper introduces a novel variational image segmentation method that uses diffeomorphic transformations and gradient fields, offering a flexible, data-efficient alternative to deep learning approaches.

Contribution

It presents a new shape analysis-based variational framework leveraging LDDMM and varifold representations for accurate image segmentation without extensive training data.

Findings

Effective segmentation achieved with the proposed method.

Framework is flexible and theoretically grounded.

Implementation with GPU acceleration enhances performance.

Abstract

Image segmentation is a fundamental task in computer vision aimed at delineating object boundaries within images. Traditional approaches, such as edge detection and variational methods, have been widely explored, while recent advances in deep learning have shown promising results but often require extensive training data. In this work, we propose a novel variational framework for 2D image segmentation that integrates concepts from shape analysis and diffeomorphic transformations. Our method models segmentation as the deformation of a template curve via a diffeomorphic transformation of the image domain, using the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework. The curve evolution is guided by a loss function that compares the deformed curve to the image gradient field, formulated through the varifold representation of geometric shapes. The approach is implemented in Python with GPU acceleration using the PyKeops library. This framework allows for accurate segmentation with a flexible and theoretically grounded methodology that does not rely on large datasets.