# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
## 6 Conclusions  
In this work, we evaluated the performances of several state-of-the-art nuclei segmentation methods based on CNNs, focusing on a challenging, real-world application scenario in which a LIS database is updated daily with images of different domains and no manually annotated images are available. To this aim, we simulated a real application scenario, performing several cross-dataset experiments by training each model on one source dataset and then testing it on a different target dataset. Our results show that some of the existing CNN models can also achieve a relatively good performance in cross-dataset scenarios, but this happens only when the target images are similar to the ones used for training, while their performance is considerably worse when target and source significantly differ in colours and scale.  
The large gap between same- and cross-dataset performances demonstrated that more efforts must be devoted to creating more generalising methods and, therefore, methods that are suitable for real applications. The attained results also suggest to avoid focusing future work on improving the cell nuclei segmentation accuracy on benchmark datasets under the same-domain scenario and to address the efforts towards achieving a higher invariance in colour and scale.  
As a possible solution to improve cross-domain effectiveness when no manually annotated data from the target scene are available, or when few images (even unlabelled) of the same domain are available to perform a DA, we envisage the use of a pre-processing procedure on the target images, in order to modify them to be more similar to the source images used for training the models. We are  
Figure 4: Visual segmentation results of the CNN-based methods trained using MIC17 and BNS datasets over an image used in the HistoDSSP project.  
currently investigating this approach, and preliminary results will soon be submitted for consideration in a journal.  
Investigation, L.P.; software, L.P.; supervision, G.F.; writing--original draft, L.P.; writing--review and editing, G.F. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the project Histopathology Decision Support Systems (HistoDSSP) based on whole slide imaging analysis, funded by Regione Autonoma della Sardegna (POR FESR Sardegna 2014-2020 Asse 1 Azione 1.1.3).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; nor in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CAD & computer-aided diagnosis \\ DSSP & decision support system for pathologists \\ LIS & laboratory information system \\ WSI & Whole Slide Image \\ H\&E & hematoxylin \& eosin \\ GBM & glioblastoma \\ LGG & lower grade glioma \\ HNSC & head and neck squamous cell carcinoma \\ LUSC & lung squamous cell carcinoma \\ DA & domain adaptation \\ UDA & unsupervised domain adaptation \\ CNN & convolutional neural network \\ RCNN & regional CNN \\ FCN & fully connected network \\ HED & holistically-nested edge detector \\ DIMAN & Deep Interval-Marker-Aware Network \\ TP & true positive \\ FN & false negative \\ FP & false positive \\ F1 & F1-score \\ P & precision \\ R & recall \\ ODI & object-level Dice index \\ OHD & object-level Hausdorff distance \\ \end{tabular}