# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 3 Context of Application
### Context-Specific Configuration of the USRL Schema  
In [PERSON] et al. [11], the authors analysed the images in the ALS dataset using the information contained in the DAPI and ChAT channels only. Following the same approach, only those two channels were considered in the instantiation of the USRL schema, MagNoLia (named after Motor Neuron Labeller); The software tool is available for download from [[https://github.com/giaracvi/MagNoLia](https://github.com/giaracvi/MagNoLia)]([https://github.com/giaracvi/MagNoLia](https://github.com/giaracvi/MagNoLia)) (accessed on 24 April 2020) under the Apache 2.0 License. For a better visualisation of the process, the representation of the output of each phase is presented and explored in Figure 5.  
The specific workflow of MagNoLia is set as follows:  
1. [label=()]
2. Object Segmentation (OS) phase: OS is performed separately in the DAPI and in the ChAT channel. Regarding the DAPI channel, the aim is to segment the nuclei and inclusions of MNs. To that end, OS is performed with two techniques: thresholding and tone-based clustering. First, thresholding is used to alleviate the negative impact  
Figure 4: Two examples of MN segmentation and labelling using ilastik. Each example takes up one row. For each example, the columns display **(a,b)** channels DAPI (coloured in blue) and ChAT (coloured in magenta) in the original image, **(c)** the result of the segmentation using ilastikand **(d)** the Ground Truth (GT) generated by an expert for that same image. In Columns **(c,d)**, teal pixels represent inclusions, orange ones represent cytoplas and purple ones represent nuclei. Figure 5: Visualisation of the output of each of the phases of MagNoLia for Image 1 TG1B1. The segmentation is restricted to two channels, the DAPI and ChAT. Column **(a)** represents the original DAPI (coloured in blue) and ChAT (coloured in magenta) inputs; Columns **(b–d)** display the output of the object segmentation, refinement and labelling phases, respectively. of very bright non-MN nuclei on the subsequent clustering; to that end, a tonal intensity threshold of 0.8 was imposed, considering only just the objects below that value. Next, tone-based clustering is based on the homogeneity of the inclusions and the MN nuclei, using \(k\)-means; the tones were separated into four different groups, and the two with medium intensities were selected. Regarding the ChAT channel, the aim is to segment the MNs. With that intent, the OS is performed by self-adapting thresholding. Specifically, we used the Rosin method, as the unimodal tonal distribution in the image matched the thresholding priors of the method [23]. * Refinement phase: REF is performed separately in each of the channels. For the DAPI channel, we discarded those objects smaller than 0.05% of the area of the image. For the ChAT channel, REF is carried out similarly, just adapting the parameters (valid size range) to MN areas. In this case, the valid size was set to 0.12-10% of the area of the image, discarding any other object. * Labelling phase: The LBL phase combines the refined objects in DAPI and ChAT channels into a single representation. The process is depicted in Figure 6. For each object detected in ChAT, a series of questions are made. First, an object in ChAT is only considered as a candidate if it overlaps objects in DAPI; this corresponds to the idea that each MN must contain a nucleus, and might also contain an inclusion. An object in ChAT is considered valid if (a) it overlaps with an object in DAPI and (b) at least 90% of the area of such an object in DAPI is coincident with the object in ChAT. Applying this condition, the candidate objects are first categorised as an MN candidate with a nucleus (just one object in DAPI within an object in ChAT) or as an MN candidate with a nucleus and inclusion (two objects in DAPI within an object in ChAT). This decision was not based exclusively on the number of objects.