Attributed Relational Graphs for Cell Nucleus Segmentation in Fluorescence Microscopy Images

Abstract

More rapid and accurate high-throughput screening in molecular cellular biology research has become possible with the development of automated microscopy imaging, for which cell nucleus segmentation commonly constitutes the core step. Although several promising methods exist for segmenting the nuclei of monolayer isolated and less-confluent cells, it still remains an open problem to segment the nuclei of more-confluent cells, which tend to grow in overlayers. To address this problem, we propose a new model-based nucleus segmentation algorithm. This algorithm models how a human locates a nucleus by identifying the nucleus boundaries and piecing them together. In this algorithm, we define four types of primitives to represent nucleus boundaries at different orientations and construct an attributed relational graph on the primitives to represent their spatial relations. Then, we reduce the nucleus identification problem to finding predefined structural patterns in the constructed graph and also use the primitives in region growing to delineate the nucleus borders. Working with fluorescence microscopy images, our experiments demonstrate that the proposed algorithm identifies nuclei better than previous nucleus segmentation algorithms.

Iterative h‐minima‐based marker‐controlled watershed for cell nucleus segmentation

“…First, we conduct experiments on the dataset that we used in our previous work (20). In this dataset, 785 nuclei are used as training instances, on which the parameters of the algorithms are selected.…”

“…The second one contains 985 nuclei taken from 16 images of the HepG2 cell line. In addition to these test sets, which were taken from our previous work (20), we form another one that contains more confluent cells. This test set contains 1,065 nuclei taken from 4 images of the HepG2 cell line.…”

“…We compare our proposed algorithm with four nucleus segmentation methods: adaptive h-minima (15), conditional erosion (22), iterative voting (10), and ARGraphs (20). The first two are marker-controlled watersheds.…”

“…The iterative voting method (10) gets image pixels iteratively voted along the radial and tangential directions to determine nucleus centers. The ARGraphs (20) method, which we previously implemented in our research group, models nucleus boundaries by an attributed relational graph and identifies nucleus centers by searching patterns on this graph. Once they identify the nucleus centers, both of these methods delineate nuclei, growing the centers by a watershed algorithm.…”

“…In (19), we defined the markers using intensity and gradient properties of the live cells specific to the KATO-3 cell line; we did not use the h-minima transform at all. Different than these watersheds, in (20), we developed a model-based segmentation algorithm that Original Article identifies the initial nucleus locations using a graphical model constructed on nucleus boundaries and grows them also using these boundaries. In this current work, we point to the problem of using a single fixed h value to identify the markers of all nuclei within the same image/component and propose a new iterative h-minima-based watershed algorithm that uses multiple h values for more accurate cell nucleus segmentation.…”