Active spline model: A shape based model-interactive segmentation

TL;DR

This paper introduces an interactive shape-based segmentation method using centripetal-parameterized Catmull-Rom splines, allowing user edits to improve segmentation accuracy across different imaging modalities.

Contribution

It formulates a point distribution model with splines to enable user interaction and post-segmentation editing, enhancing segmentation accuracy.

Findings

Achieved an average overlap of 0.879 in lung segmentation.

User edits improved overlap to 0.945.

Method demonstrated across three imaging modalities.

Abstract

Rarely in literature a method of segmentation cares for the edit after the algorithm delivers. They provide no solution when segmentation goes wrong. We propose to formulate point distribution model in terms of centripetal-parameterized Catmull-Rom spline. Such fusion brings interactivity to model-based segmentation, so that edit is better handled. When the delivered segment is unsatisfactory, user simply shifts points to vary the curve. We ran the method on three disparate imaging modalities and achieved an average overlap of 0.879 for automated lung segmentation on chest radiographs. The edit afterward improved the average overlap to 0.945, with a minimum of 0.925. The source code and the demo video are available at http://wp.me/p3vCKy-2S