# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 3 Open Issues and Goal of this Work  
As previously reported, cell nuclei segmentation in histopathology images has been extensively studied using a variety of methods. Most work has focused on developing nuclei segmentation methods for single tissues and specific applications, while others focused on more generic application by developing nuclei segmentation methods for multiple tissues. However, there is still a lack of training datasets for certain organs, and even the single-tissue and multi-tissue datasets do not provide an exhaustive overview of the possible morphological variations of the cells caused by a greater disease grade. Furthermore, even images belonging to the same organ could present different appearances due to chromatic stain variability, non-homogenous background and variability in optical image quality caused by various digital microscopes or scanners [1; 28]. An example of such a chromatic issue is shown in Figure 1.  
Recently, some authors tried to incorporate the benefits of the unsupervised domain adaptation (UDA) methods to tackle these issues. UDA methods allow adapting a model trained on a source domain to another target domain by exploiting some target images without annotations [50; 51; 52; 53; 54]. Such methods achieve state-of-the-art performance on UDA on object detection and classification. However, they are not specifically designed for semantic segmentation; thus, they ignore the domain shift at the semantic level. Indeed, several researchers exploiting UDA have, in their respective studies, proposed for semantic segmentation on many medical images using domain-invariant intermediate features [55] or both pixel- and feature-level adaptations with [29; 30; 54; 56]. Among them, only two methods are designed for cell nuclei segmentation, providing an adaptation only at a pixel level [29] or at both the pixel and feature levels [30].  
Although some solutions based on domain adaptation (DA) have already been proposed, they are mainly inspired by other fields of applications, neglecting most of the issues that can be present in this field, especially in real applications. In particular, they all require representative, although unlabelled, images of the target domain, which is not feasible in the real application scenario considered in this work. In this scenario we are dealing with in the HistoDSSP project, a system has to be deployed on a target image database for which it is not possible to collect annotations for training or fine-tuning; additionally, the target image database is updated daily with images belonging to various laboratories. In other words, just a few images of the same domain will be available to perform the adaptation.  
Furthermore, only limited cross-dataset evaluations were provided in the respective papers, and most of the experiments focused on the adaptation from fluorescence microscopy images to H&E stained histopathology images [30], which is quite far from a real application. A realistic cross-dataset evaluation for cell nuclei segmentation is, therefore, still lacking in the literature. This is, however, a fundamental issue which must be addressed in order to be able to develop nuclei segmentation methods in real applications. Accordingly, this work aims to provide a comprehensive evaluation on the performance gap of state-of-the-art cell nuclei segmentation methods between same-dataset and cross-dataset scenarios, i.e., where manually annotated images of the target dataset are or are not available for training, respectively.