## 5 Conclusions  
The results show that using more complex algorithms for this problem gives us better performance, but interestingly the increment is in the sensitivity measure. Precision was already high with even the simplest algorithm proposed by us a good few years ago. We were also able to show that the sample density, being an interesting factor in developing Algorithm 1, is less important for these more complex algorithms. They perform similarly  
Figure 12: Graphical comparison of Algorithms 1 (blue) and 3 (red) to the FCM (green) reference. The peak ratios for the 15 samples in two equivalent representations; the datasets are marked with the same color on both plots. across the samples in that regard (though we have seen cases where there is still room for improvement). The results also show (Figure 12 and Table 7) that using a more accurate segmentation algorithm increases the accuracy (similarity to the reference FCM measurements) of results at the DNA histogram level. Examining the samples #11 and #15, and comparing the algorithms regarding their accuracy in segmenting objects of different intensities is also something that could be worth pursuing, especially because during visual inspection multiple cases were encountered where the annotations were self-contradictory in the case of low-intensity objects. Revision of the ground truth both in location and intensity might be considered based on the algorithms' findings. Investigating the possibility to upgrade the ground truth data automatically, utilizing the segmentation results generated by multiple algorithms seems a challenging, though viable route for improvement. While more complex algorithms such as U-Net, Mask R-CNN and DeepLab v3+ could potentially enhance segmentation accuracy, the available data may limit the achievable improvements (the non-significant difference between Algorithms 2 and 3 might be a precursor to this). There may be diminishing returns where increasing algorithm complexity yields only marginal accuracy gains, suggesting that the dataset itself--such as annotation quality or sample variability--may hinder further significant performance improvements, despite using more advanced models. A focused study is needed to directly compare the (earlier) proposed calibration technique to current calibration practices, particularly using a greater number of samples. Such a study would provide more robust statistical validation of the system's accuracy and reliability, helping to confirm its suitability for broader clinical adoption. By expanding the sample size and including various biological conditions, such research could better evaluate the technique's effectiveness in real-world settings. It is interesting to note that though annotation time took 20-40 min per sample depending on object count, all three algorithms took only seconds to run on the same region, having a clear time benefit. Adding that to the scanning time of a mean six and a half minutes (though the object segmentation step is only a part of the task), seems to be comparable to the reference technique. The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app14198707/s1](https://www.mdpi.com/article/10.3390/app14198707/s1)]([https://www.mdpi.com/article/10.3390/app14198707/s1](https://www.mdpi.com/article/10.3390/app14198707/s1)). Conceptualization, V.Z.J. ; methodology, V.Z.J. ; software, V.Z.J. ; validation, V.Z.J. ; formal analysis, V.Z.J. ; writing--original draft preparation, V.Z.J. ; writing--review and editing, R.P. and M.K. ; supervision, B.M. and M.K. ; funding acquisition M.K. All authors have read and agreed to the published version of the manuscript. This research received external funding from the national project 2019-1-3-1-KK-2019-00007. This study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of the Institutional Review Board and Regional Ethics Committee (TUKEB) (permit no.