Split and Merge Watershed: a two-step method for cell segmentation in fluorescence microscopy images.

The development of advanced techniques in medical imaging has allowed scanning of the human body to microscopic levels, making research on cell behavior more complex and more in-depth. Recent studies have focused on cellular heterogeneity since cell-to-cell differences are always present in the cell population and this variability contains valuable information. However, identifying each cell is not an easy task because, in the images acquired from the microscope, there are clusters of cells that are touching one another. Therefore, the segmentation stage is a problem of considerable difficulty in cell image processing. Although several methods for cell segmentation are described in the literature, they have drawbacks in terms of over-segmentation, under-segmentation or misidentification. Consequently, our main motivation in studying cell segmentation was to develop a new method to achieve a good tradeoff between accurately identifying all relevant elements and not inserting segmentation artifacts. This article presents a new method for cell segmentation in fluorescence microscopy images. The proposed approach combines the well-known Marker-Controlled Watershed algorithm (MC-Watershed) with a new, two-step method based on Watershed, Split and Merge Watershed (SM-Watershed): in the first step, or split phase, the algorithm identifies the clusters using inherent characteristics of the cell, such as size and convexity, and separates them using watershed. In the second step, or the merge stage, it identifies the over-segmented regions using proper features of the cells and eliminates the divisions. Before applying our two-step method, the input image is first preprocessed, and the MC-Watershed algorithm is used to generate an initial segmented image. However, this initial result may not be suitable for subsequent tasks, such as cell count or feature extraction, because not all cells are separated, and some cells may be mistakenly confused with the background. Thus, our proposal corrects this issue with its two-step process, reaching a high performance, a suitable tradeoff between over-segmentation and under-segmentation and preserving the shape of the cell, without the need of any labeled data or relying on machine learning processes. The latter is advantageous over state-of-the-art techniques that in order to achieve similar results require labeled data, which may not be available for all of the domains. Two cell datasets were used to validate this approach, and the results were compared with other methods in the literature, using traditional metrics and quality visual assessment. We obtained 90% of average visual accuracy and an F-index higher than 80%. This proposal outperforms other techniques for cell separation, achieving an acceptable balance between over-segmentation and under-segmentation, which makes it suitable for several applications in cell identification, such as virus infection analysis, high-content cell screening, drug discovery, and morphometry.