A first challenge is the reliable separation between signal originating from chloroplasts and background noise. Autoadaptive strategies are used to compute suitable thresholds. A second challenge is the detection of individual chloroplasts and their separation from other chloroplasts in the immediate vicinity. We detect potential chloroplast seeds and employ local thresholds and shape criteria to successively grow these seeds while keeping them separated from adjacent chloroplast seeds (Figure 2).
Chloroplasts are detected in 3D to allow volume measurements and the assessment of shape parameters. The third challenge is the tracking of chloroplasts over time. Individual chloroplasts need to be followed throughout the 4D data set in order to from the close spatial proximity of chloroplasts, morphological similarities between neighbor in the next time frame and successively increase the search range to ensure optimal linking (Figure 3).
We demonstrate that fully automated tracking of chloroplasts in Elodea leaf cells from 4D image data is possible using Definiens Cognition Network Technology. We are confident that the strategies we employed are readily applicable to many other biological image analysis tasks.
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