# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 1 Introduction  
Cell nuclei segmentation from stained tissue specimens is a useful computer vision functionality in histopathological image analysis [1; 2]. Unlike cell nuclei detection, it provides whole nuclei binary masks that it can be used not only to extract the number and distribution of the cells but also to extract detailed information of each nucleus, such as colour, shape and texture features [3; 4; 5]. This information is helpful since it allows to count normal or cancer cells and recognise tissue structures, and because it is critical in cancer identification, grading, and prognosis [6; 7; 8; 9]. The analysis of tissue specimens is performed after a staining procedure that highlights the interesting components of tissues [10]. The most used stain materials are hematoxylin & eosin (H&E) [11; 12], which impart the blue-purple and pink colour to the nuclei and cytoplasm, respectively. For more than a century and still today, the interpretation of tissue specimens is performed under a microscope by human experts [10; 13; 14]. Nevertheless, this task requires a substantial manual effort for pathologists, whose results are subjective, as they are influenced by the level of expertise and tiredness, even though accuracy and efficiency are crucial in this field. The growing need for objective and efficient automatic techniques has attracted the attention of many researchers in this field; these researchers have employed computer vision techniques to create computer-aided diagnosis (CAD) tools [4; 15] for improving work efficiency and reducing error rates for pathologists [16; 17]. In this paper, we focus on the nuclei segmentation task--in particular, on convolutional-neural-networks (CNNs)-based methods, that have recently demonstrated state-of-the-art performance [18; 19]. Indeed, more straightforward approaches, such as thresholding,are efficient, but they fail with noisy images or colour variations [2; 20], while more advanced image segmentation methods such as deformable models are complicated and computationally expensive [2; 21]. However, despite the considerable effort spent so far by the research community, and the performance improvements achieved by current methods based on CNNs on benchmark datasets [22; 23], cell nuclei segmentation remains a challenging task [24; 25; 26] due to chromatic stain variability, non-homogenous background, nuclear overlap and occlusion and differences in cell morphology and stain density [2; 19; 27]. This task is even more challenging in real applications where the specimens could belong to different tissue types. A further variability in optical image quality comes from the use of various staining protocols and digital microscopes or scanners [1; 28]. We focus on this challenging application scenario which was inspired by our work in the recent HistOSSP (Histopathology Decision Support Systems for Pathologists, based on whole slide imaging analysis) project, aimed at the design and implementation of a prototype of decision support system for pathologists (DSSP) to be integrated into a laboratory information system (LIS). In particular, we consider fully unsupervised application scenarios in which 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 different laboratories. Although considerable progress has been achieved so far and some solutions based on domain adaptation (DA) or transfer learning have already been proposed [29; 30], cell nuclei segmentation remains a challenging task in the setting mentioned above. In particular, only limited cross-dataset evaluations of existing methods have been provided in the respective papers, partly due to the small number and tiny size of publicly available datasets, but mainly because the authors focused their attention on DA problems similar to other fields of applications, neglecting the real DA problems that can be present in this field of application.