## 4 Discussion and Conclusions  
The fully automatic pipeline for cell detection and counting proposed in this paper, called ACDC, exploits watershed transform [39; 40] and morphological filtering operations [41; 42], and also benefits from the edge-preserving smoothing achieved by the bilateral filtering [38]. Notably, this pipeline does not depend on expert knowledge and can be deployed by setting only a few parameters. The capabilities of ACDC were tested on two different cell imaging datasets characterized by significantly different acquisition devices, specimens, image characteristics, and experimental conditions. ACDC was shown to be accurate and reliable in terms of cell counting and segmentation accuracy, thus representing a laboratory feasible solution also thanks to its computational efficiency. As a matter of fact, the Pearson coefficient achieved both in the case of the VU and DSB datasets was higher than 0.96, demonstrating an excellent agreement between automated cell count achieved with ACDC and the manual gold standard. The performance of ACDC were also compared with two pipelines defined with ImageJ; our results highlighted that while the cell counting was comparable, ACDC allowed us to achieved better segmentation outcomes. Moreover, we considered an additional cell images dataset and compared the outcome achieved with ACDC against CellProfiler, Marker-Controlled Watershed and Split and Merge Watershed, further showing the reliability of ACDC for what concerns segmentation and cell counting results. Finally, with the current ACDC implementation, it is also possible to distribute the computation on multi-core architectures and computer clusters, to further reduce the running time required to analyze large single image stacks. To this end, an asynchronous job queue, based on a distributed message passing paradigm was developed exploiting Celery and RabbitMQ. The time-lapse microscopy image samples analyzed in this study are considerably different from to the publicly available microscopy images of cell nuclei, such as in [75; 76; 77]. Indeed, these datasets were captured under highly different microscope configurations compared to the VU experiments; with particular interest to magnification, a 40\(\times\)/0.9 numerical aperture and a 20\(\times\)/0.8 objective lens were used in [75; 76; 77], respectively. These acquisition characteristics, summarized in Table 4, produce substantially different images, compared to the datasets analyzed in this work, as in the case of the average coverage (i.e., the percentage of pixels covered by cell nuclei), which is remarkably different. As a future development, we plan to improve ACDC, to achieve accurate cell nuclei detection and analysis for different cell imaging scenarios, independently from the acquisition methods. We also aim at exploiting the most recent Machine Learning techniques [78] to accurately refine the segmentation results. The improvements may be achieved by classifying the geometrical and textural features extracted from the detected cells [79]. This advanced computational analysis can allow us to gain biological insights into complex cellular processes [17]. Finally, as a biological application, in the near future we plan to focus on the Fluorescent, Ubiquitination-based Cell-Cycle Indicator (FUCCI) reporter system for investigating cell-cycle states [45], by combining accurate nuclei segmentation results, on multiple fluorescent proteins for cell labeling [80], and live-cell imaging of cell cycle and division. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **U2 OS**[76] & **NIH3T3**[77] & **VU** & **DSB**[37; 46] \\ \hline \# images & 48 & 49 & 46 & 301 \\ Image size & \(1349\times 1030\) & \(1344\times 1024\) & \(2160\times 2160\) & various \\ Magnification & \(40\times\) & \(40\times\) & \(10\times\) & various \\ Total \#cells & 1831 & 2178 & 14,045 & 7931 \\ Min. \#cells & 24 & 29 & 22 & 1 \\ Max. \#cells & 63 & 70 & 1763 & 124 \\ Avg. \%coverage & 23\% & 18\% & 2.07\% & 5.41\% \\ \hline \hline \end{tabular}
\end{table}