# A Smartphone-Based Cell Segmentation to Support Nasal Cytology
## 5 Experimental Results  
The cell extractor here described has been tested on 75 digital images representing fields, first performing a standard cell observation and manual counting for each field, and then taking into consideration the cells detected through the system proposed in this paper.  
In Figure 12 a qualitative example of the working system for one of the 75 images is reported. The result in terms of the detected cells is shown with a blue bounding box around the segmented objects. The performance of this system is reported in Table 1--all cells and non-cells on the 75 slides were also manually labeled by domain experts, to obtain the ground truth.  
\begin{table}
\begin{tabular}{c c c} \hline \multicolumn{3}{c}{**Confusion Matrix**} \\ \hline \multicolumn{3}{c}{**True Condition**} \\ \hline Predicted & positive & negative \\ \hline
**Positive** & 1224 & 166 \\ \hline
**Negative** & 52 & 113 \\ \hline \end{tabular}
\end{table}
Table 1: Cell detecting performance.  
Figure 11: Cell extraction pipeline with methods—see “detect()” in Appendix B.  
Figure 9: Original image (**left**) and extracted cells (**right**).  
With reference to Table 1, TP represents cells correctly extracted, FN lost cells, FP non-cells improperly extracted, and TN non-cells discarded.  
Starting from these assumptions, the system performances are summarized here:  
Accuracy 0.860  
Sensitivity (Recall) 0.959  
Specificity 0.405  
Precision 0.881  
F-score 0.918  
The measure that must mainly be taken into consideration is certainly sensitivity, which quantifies the avoidance of false negatives. The value 0.96 is satisfactory because it shows that actual positives are not overlooked, as false negatives are few. The FN detected refer, for the vast majority, to heavily-massed cells that the same experts do not consider during the observation. In fact, the manual protocol defined by the experts is tolerant of the typical presence of clusters and specifies that at least 50 fields must be taken into account, increasing them during the observation if they find the excessive presence of clusters or almost empty fields. All of this takes significant effort. In reference to this, and also with the system we have designed, the specialist can increase the number of fields to be acquired and analyzed, proving to be flexible. Even the number of false positives does not worry us because the cells and the other \"objects\" extracted are classified manually or through the Rhino-cyt platform, which discards the FP with great accuracy.  
A final remark should be given about the execution time. Time to process a set of 50 fields manually may exceed half an hour or more. It depends largely on the expertise of the specialist and on the specific field density and cell agglomeration [57].  
Time to process a single field automatically may vary depending on how dense the field is. We observed an elapsed time of 4.2 s to process the field in Figure 4, 4.1 s to process the field in Figure 5, and 2.1 s for the field in Figure 9. We estimated the average processing time on 10 slides of differing densities, obtaining \(2.9\pm 1.1\) s. This result was obtained with a low-end/low-cost smartphone, Xiaomi Redmi Note 7, but of course, it largely depends on the device hardware. In this phase, we really focused on demonstrating the feasibility of the proposed approach in terms of segmentation effectiveness (i.e., the extraction of the cells and getting the approval of specialists about the efficacy and usefulness of this system). Then, it is worthwhile to invest in research and the development of technologies, such as those presented in this paper, while software efficiency can be pursued, but it might not be necessary, given that higher-end smartphones are increasingly more powerful and cheaper.