Seed-Point Detection of Clumped Convex Objects by Short-Range Attractive Long-Range Repulsive Particle Clustering
This work addresses the challenge of automated analysis in biological imaging and unsupervised clustering, particularly for overlapping objects and rare classes, offering a practical solution with measurable gains.
The paper tackles the problem of locating centers of overlapping convex objects, such as clumped cell nuclei, by developing a novel clustering method based on short-range attractive and long-range repulsive particle interactions. The method achieves an 8.2% improvement in F1 score over existing methods for nuclei detection and successfully identifies cluster centers in unsupervised learning tasks where other techniques fail.
Locating the center of convex objects is important in both image processing and unsupervised machine learning/data clustering fields. The automated analysis of biological images uses both of these fields for locating cell nuclei and for discovering new biological effects or cell phenotypes. In this work, we develop a novel clustering method for locating the centers of overlapping convex objects by modeling particles that interact by a short-range attractive and long-range repulsive potential and are confined to a potential well created from the data. We apply this method to locating the centers of clumped nuclei in cultured cells, where we show that it results in a significant improvement over existing methods (8.2% in F$_1$ score); and we apply it to unsupervised learning on a difficult data set that has rare classes without local density maxima, and show it is able to well locate cluster centers when other clustering techniques fail.