This choice holds possible benefits: lab desk space, a fixed cost, can be reduced by consolidating tools, thereby reducing maintenance expenses. Small diagnostic labs considering digital pathology or handling a higher imaging workload, may opt for the ICM approach as a replacement for FCM. For those adopting ICM, the introduction of ploidy analysis is streamlined, requiring only the extension of the sample preparation process to include placement on a glass slide and coverslipping. For labs with a heavier imaging workload, a glass-slide-only workflow becomes feasible. Additionally, the benefit of extended specimen storage, whether as a glass slide or in this case as a digital image, is crucial for future research projects, case consultation or teaching. Measuring the overall accuracy of an image analysis algorithm can be defined on multiple levels, and through multiple metrics. In this study we applied two approaches: one is to measure the segmentation algorithms' raw accuracy compared to the ground truth; the other is to measure the population-level DNA content's similarity to the FCM method. In our previous work, we proposed a calibration method to align image cytometry (ICM) results with those of flow cytometry (FCM) for DNA ploidy analysis, using healthy samples with known properties as references. The calibration approach involves analyzing the DNA content histograms from both methods, with the goal of developing a transfer function that ensures ICM can replicate the accuracy of FCM. Specifically, healthy samples containing at least two object populations--one with theoretically double the DNA content of the other--serve as a reference point, with these populations represented by their mean values on the DNA histogram's \(x\)-axis. Ploidy analysis examines these peaks, and their  
Figure 1: The current state of the project that is the greater environment of this study. Creating an ICM-based approach to parallel flow cytometry regarding ploidy analysis, with option for calibration. relation allows for an evaluation of the accuracy of our technique. In the current study, we extend this work by focusing on optimizing nucleus segmentation, a key step in enhancing the accuracy of ICM histograms, thereby further improving the calibration process and ensuring better alignment with FCM. The image analysis pipeline is usually comprised of a segmentation step to localize the nuclei on the sample. A separation step of some kind is necessary. The sample being a solution, the nuclei tend to form groups or clumps. Those are frequently segmented as one single object, thus greatly influencing the detection quality. After that, features are measured, like integrated optical density which represents the amount of DNA content of an object. These features then can be used to classify the nuclei, and consequently conclude the ploidy analysis: classify the cell population to be normal, or whether some deviation can be detected. Comparison of FCM and our proposed ICM method was published in [9]. This article focuses on the object detection/segmentation part of the process, highlighted in Figure 2.  
The quality of such an algorithm is usually measured via comparison to a reference or ground truth. This type of information is hard and costly to obtain, though public datasets for similar purposes can be found [10]. The ground truth dataset was created to establish a means of measurement for the counting problem, to be able to count the matching pairs of annotation and detected object along with all the other remaining cases. The resulting ground truth is a set of coordinate pairs that mark the location of each object (nucleus) visible on the digital sample. In this article we aim to evaluate the algorithms examined regarding their object detection capability. Our priority goal was to evaluate object localization, but not extent. We propose that by increasing object segmentation performance, the similarity between the FCM and ICM results will also measurably increase.