# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 4 Experiments
### Results and Discussion  
MagNoLia was applied to the ALS-GT image dataset presented in Section 3, an illustration of which was already shown in Figure 5. The biological restrictions imposed in the LBL phase were schematically represented in Figure 6, showing that the objects were either labelled as \"MN with nucleus\" or as \"MN with nucleus and inclusion\". Figure 7 displays four images from the ALS-GT dataset, together with the segmentation by ilastik, MagNoLia and a human labeller. Note that, for a better visualisation of the original images, the DAPI and ChAT channels are combined here in different colour palettes, instead of being displayed separately. Figure 7 points out some interesting facts. Overall, we can observe that MagNoLia labels a smaller number of objects than ilastik does. Furthermore, it is evident that ilastik does not enforce the cells to be biologically viable. Otherwise said, while the detection in MagNoLia forces the selected areas to represent viable MNs, the results by ilastik impose no such restriction. For example, ilastik labels some independent objects not contained  
Figure 7: Visual representation of the output by ilastik, MagNoLia and human experts for four different images in the dataset. Each row contains the results for a single image. Column (**a**) displays an overlap of the DAPI (coloured in blue) and ChAT (coloured in magenta) channels; Columns (**b–d**) display the output by ilastik, MagNoLia and the human labeller, respectively. In Columns (**b–d**), teal pixels represent inclusions, orange ones represent cytoplasms and purple ones represent nuclei. in any MN as inclusions or nuclei. These cases are not labelled by MagNoLia, since they are biologically non-viable, due to the first condition in the LBL phase (\"Overlapping objects in DAPI and ChAT?\"). From a qualitative point of view, ilastik is likely to detect more MNs, at the cost of losing precision and confidence in the results. Even when comparing cases where both approaches failed, MagNoLia seemed to show a higher precision than ilastik. Note that, in medical imaging, as experts deal with sensitive data, there is a preference to preserve only the most trustworthy information in image segmentation and classification processes, i.e., to preserve only what one is most confident about, even if that entails a loss of information in the process. This implies that restrictive methods are preferred over more permissive ones, since the latter, in order to account for more TP (obtaining a higher Rec), might also allow for more FP (entailing a lower Prec). Measures derived from a confusion matrix are well tailored for such an analysis and, consequently, were applied as performance measures to compare the results obtained by MagNoLia and ilastik with the hand-labelled ground truth. The quantitative comparison of the results is summarized in Tables 1 and 2. In Table 1, we display the average Prec, Rec, and F for the global detection of MNs in the dataset, while Table 2 contains the results considering each individual subregion of the MNs. In this manner, Table 1 represents the global detection of MNs, and Table 2 illustrates the performance in finely detecting the components within them. Additionally, Figure 8 displays a more detailed view of the results in Tables 1 and 2. Specifically, this figure displays the individual results (in terms of Prec and Rec) of each method and each image, as well as the mean for both ilastik and MagNoLia. Regarding the global results in Table 1, we observed that MagNoLia had a higher Prec than ilastik. We can, hence, assert that MagNoLia meets the objective of providing a more trustworthy segmentation, which can be quantified in a 50% increment in Prec compared to ilastik. While less important than Prec, Rec is also a representative performance measure. As seen in Table 1, ilastik obtained a higher Rec, which means that it is in fact able to recognise and retrieve more objects than MagNoLia does. The trade-off between Rec and Prec can be visually seen on image 1 TG1B1 in Figure 7a.