# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 2 Related Works  
A variety of standard image analysis methods are based on H&E image analysis in order to extract cell nuclei [31; 32], with some based on detection [33; 34] and other based on segmentation [35; 36]. For the reasons mentioned above, segmentation-based methods, including segmentation methods that exploit thresholding, clustering, watershed algorithms, active contours and CNNs, have been preferred over detection-based ones. A variety of pre- and post-processing techniques have also been extensively used--in particular, in combination with simpler segmentation techniques [4]. Automatic-thresholding-based methods were the most used cell nuclei segmentation techniques in the early stage of this research area [2], including single Otsu thresholding [37] or multi-level Otsu thresholding [38]. In most cases, the segmentation results, obtained by thresholding, were refined through morphological operations such as morphological reconstruction [20] or multiple morphological operations [39]. Even the watershed transformation [40] was used for nuclei segmentation, but mostly as a post-processing method for overlapping objects segmentation. It was used both in its classical version, coupled with the distance transform [41], and in its marker-controlled version, where the markers are extracted with a conditional erosion [23; 42]. However, the segmentation results of thresholding-based methods could be heavily affected by chromatic stain variability and a non-homogenous background. For these reasons, more advanced image segmentation methods have been proposed over the years. For instance, the active contour model has been combined with the expectation-maximisation (EM) algorithm [43]. Different level set methods have been used to delineate the cell boundaries, both by using a multi-phase approach [44] and exploiting the mean shift clustering as the initialisation points [1]. Even the graph-cut method was used to segment the cell nuclei coupled with an improved Laplacian-of-Gaussian filter to detect the seed points [24]. Nevertheless, the methods mentioned above could be complex and too sensitive to the initialisation and setting parameters. Various machine-learning-based nuclear segmentation approaches have been investigated to better tackle this issue and the variation over cell morphology and colour. The former type of machine-learning-based method exploits handcrafted features, such as filter bank responses, geometric and texture descriptors, that are forwarded to standard classification algorithms [45; 46]. Recently, the great success of deep learning methods gave birth to a new type of machine-learning-based methods for nuclei segmentation, achieving outstanding performances [2; 19; 22; 28; 47]. In these methods, the images are directly used to feed CNN to label pixels as nuclear (foreground) or non-nuclear (background). Even with CNN-based methods, some additional post-processing operations have been used to separate the overlapping or touching nuclei in the foreground regions by using the shape deformation method [19] or the watershed transformation [23; 28; 36]. Other approaches instead added a third class for the nuclei boundary to define a precise separation between background and foreground regions, especially between touching nuclei, avoiding the post-processing procedures. The boundary region can be directly used to separate the touching nuclei into individual ones by removing the boundary from the foreground [47], or it can be used as a constraint for a growing region that starts from the foreground seed points [22]. Unfortunately, CNN-based methods require massive amounts of annotated and representative training data. The amount of the required training data is even larger for a network deployed in a real application for the segmentation of nuclei from various organs. As a consequence, when training data is scarce or not representative of deployment scenarios, CNNs may also suffer from overfitting to a different extent, and may hardly generalise to images coming from various tissues.