Refinement-Cut: User-Guided Segmentation Algorithm for Translational Science

TL;DR

Refinement-Cut is a semi-automatic, user-guided segmentation algorithm designed for real-time interactive medical image analysis, allowing rapid seed placement to improve segmentation in challenging cases without losing real-time feedback.

Contribution

The paper introduces a novel user-guided seed placement method that enhances interactive segmentation accuracy in difficult medical imaging scenarios.

Findings

Effective in challenging homogeneous and noisy regions

Supports real-time feedback with minimal user input

Validated on clinical 2D and 3D medical data

Abstract

In this contribution, a semi-automatic segmentation algorithm for (medical) image analysis is presented. More precise, the approach belongs to the category of interactive contouring algorithms, which provide real-time feedback of the segmentation result. However, even with interactive real-time contouring approaches there are always cases where the user cannot find a satisfying segmentation, e.g. due to homogeneous appearances between the object and the background, or noise inside the object. For these difficult cases the algorithm still needs additional user support. However, this additional user support should be intuitive and rapid integrated into the segmentation process, without breaking the interactive real-time segmentation feedback. I propose a solution where the user can support the algorithm by an easy and fast placement of one or more seed points to guide the algorithm to a satisfying segmentation result also in difficult cases. These additional seed(s) restrict(s) the calculation of the segmentation for the algorithm, but at the same time, still enable to continue with the interactive real-time feedback segmentation. For a practical and genuine application in translational science, the approach has been tested on medical data from the clinical routine in 2D and 3D.