# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 4 Experiments
### Results and Discussion
In this image, while MagNoLia detects a single MN (and its contents), ilastik detects them all, at the cost of poor discrimination of spurious objects. The results considering each of the individual parts of the cell were also favourable to MagNoLia. In fact, MagNoLia obtained a higher Prec for all possible subregions of the \begin{table}
\begin{tabular}{c c c} \hline \hline  & ilastik & magnolia \\ \hline Prec & 0.346 & **0.537** \\ Rec & **0.467** & 0.324 \\ F & 0.398 & **0.404** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average quantitative results obtained by ilastik and MagNoLia on the images in the ALS-GT dataset for the segmentation of MNs. Each row corresponds to a different performance measure, and the best results for each performance measure are put in boldface for better visualisation. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Nuclei**} & \multicolumn{2}{c}{**Cytoplasms**} & \multicolumn{2}{c}{**Inclusions**} \\ \cline{2-7}  & ilastik & MagNoLia & ilastik & MagNoLia & ilastik & MagNoLia \\ \hline Prec & 0.348 & **0.438** & 0.439 & **0.513** & 0.286 & **0.689** \\ Rec & **0.390** & 0.238 & 0.364 & **0.375** & **0.697** & 0.269 \\ F & **0.367** & 0.308 & 0.398 & **0.433** & **0.406** & 0.387 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average quantitative results obtained by ilastik and MagNoLia on the images in the ALS-GT dataset for the segmentation of each subregion of an MN. Each row corresponds to a different performance measure, and the best results for each type of subregion and performance measure are put in boldface for better visualisation. MNs. In fact, the improvement w.r.t. ilastik was significant, especially in the case of the inclusions. As was the case for the results in Table 1, ilastik is generally better than MagNoLia in terms of Rec, while both are comparable in terms of F.  
A detailed view into the results is given in Figure 8. In this figure, we observe the distribution of the results that lead to the averaged quantities in Tables 1 and 2. Overall, it was clear that MagNoLia tended to produce a greater precision than ilastik, at the cost of a lower recall. This can be clearly seen in Figure 8a, but also in the results for individual cell regions, especially in Figure 8c. A more detailed analysis brought forward facts that could not be seen in Tables 1 and 2. Interestingly, ilastik tended to produce results grouped around the mean, while MagNoLia did not. While in Figure 8a,c, the mean result by MagNoLia is influenced by similar results for individual images, this is not the case in Figure 8b,d, where Prec tends to have extreme values for individual images. Observing the results in Figure 8, the validity of the mean as the global evaluation of the results by each method might be questioned in favour of other alternatives (such as the median). However, the mean does embody the idea of measuring the amount of information obtained across the dataset. Discarding outliers, or using the median, might hinder the understanding of the amount of information retrieved by each of the methods across the dataset. It is, however, very relevant to know that the mean Prec obtained by MagNoLia was, occasionally, produced by a combination of very high and low results, not necessarily grouped around the mean (especially for the inclusions, as can be seen in Figure 8d). As a general conclusion, we can state that MagNoLia provided better results than ilastik, given that Prec is preferred over Rec in the context of neural tissue analysis. Figure 8: Distribution of results for ilastik and MagNoLia according to the type of subregion. Plot (**a**) represents the Prec and Rec for all subregions, while Plots (**b–d**) represent the precision and recall of each subregion of an MN. The concentric circles represent, for each plot, the mean (centre of mass) of the point clouds. In quantitative terms, MagNoLia succeeded in ensuring a higher precision than ilastik.