Seeded Watersheds for Combined Segmentation and Tracking of Cells

Abstract

Abstract. Watersheds are very powerful for image segmentation, and seeded watersheds have shown to be useful for object detection in images of cells in vitro. This paper shows that if cells are imaged over time, segmentation results from a previous time frame can be used as seeds for watershed segmentation of the current time frame. The seeds from the previous frame are combined with morphological seeds from the current frame, and over-segmentation is reduced by rule-based merging, propagating labels from one time-frame to the next. Thus, watershed segmentation is used for segmentation as well as tracking of cells over time. The described algorithm was tested on neural stem/progenitor cells imaged using time-lapse microscopy. Tracking results agreed to 71% to manual tracking results. The results were also compared to tracking based on solving the assignment problem using a modified version of the auction algorithm.

High throughput microscopy: from raw images to discoveries

“…An example is shown in Fig. 2M-P. Several variations of the watershed algorithm have been successfully applied to cell images (Pinidiyaarachchi and Wahlby, 2005;Wahlby and Bengtsson, 2003). Both the simple threshold and watershed methods rely on the generation of a binary mask by identifying an optimal threshold that can separate cells from background.…”

Cell Lineage Construction of Neural Progenitor Cells

“…The non-uniform illumination present in the microscopic images is removed using homomorphic filter [8], anisotropic diffusion method [9], least square technique [10], Ridgelet transform method [11] and Gaussian filter [12]. L.B.…”

“…segmenting the cells in the first frame and then tracking these cells throughout the sequence by updating the cell position. The methods based on active contours [31,32,33], auction algorithm [10], level set method [34,25] and nearest neighbor and correlation matching technique [9] adopted the second approach. This approach could not handle cell division because additional heuristics are required to handle the new cells produced by cell division [8].…”

Dense nuclei segmentation based on graph cut and convexity–concavity analysis

“…Unfortunately, most microscopic images have a lot of noise and the contrast of the foreground (cell/nucleus) with the background is rather small whereas the variance within the foreground (cell/nucleus) is rather large. Other promising Tel: +01 773 742 1699; fax: +01 847 491 4455; e-mail: jqichina@hotmail.com; j-qi@northwestern.edu methods for cell or nuclei foreground segmentation include seeded watershed algorithms (Pinidiyaarachchi & Wahlby, 2005;Cheng & Rajapakse, 2009), a supervised machine learning method (Kong et al, 2011), a level set active contour model (Harder et al, 2011), multiscale analysis , dynamic programming-based methods (Baggett et al, 2005;McCullough et al, 2008), Markov random fields (Luck et al, 2005) and graph-cut methods (Boykov & Funka-Lea, 2006;Danek et al, 2009;Al-Kofahi et al, 2010). We provide a short review on these methods in the following.…”

“…Seeded watershed segmentation (Pinidiyaarachchi & Wahlby, 2005;Cheng & Rajapakse, 2009) uses seeds as markers to try to overcome the oversegmentation and undersegmentation problems occurring in basic watershed algorithm (Vincent & Soille, 1991). In these methods, the watershed transformation is performed by defining a singular seed or marker for each cell or nuclei object.…”