# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 3 Context of Application
### Testing Dataset  
The dataset was collected in the context of a study focused on assessing the localisation of different ALS-related proteins within the spinal Motor Neurons (MNs) of healthy individuals and ALS patients. Protein mislocalisation is one of the hallmarks of many neurodegenerative disorders and other diseases [43], and protein transfer from the nucleus to the cytoplasm is essential for molecular pathology. This dataset was previously used to determine the co-mislocalisation of different RNA-binding proteins associated with ALS [12] in a manual way and subsequently used to develop an automated analysis pipeline. This dataset presents several challenges in the identification of MNs and their segmentation: (i) MNs are large cells with a ramified morphology, which is not always conserved during tissue sectioning; (ii) even though the dataset has specific MN-exclusive molecular markers, there is sometimes an overlap between the MNs of interest and other cell types stained for the same markers; for example, when analysing the DNA staining to segment nuclei and cell bodies; (iii) tissue sections present a variable level of fluorescent background, which makes it sometimes difficult to distinguish MNs from the surrounding tissue; (iv) MNs sometimes present corpora amylacea (i.e., inclusions), which are age-related, insol  
Figure 2: Schematic representation of the USRL schema. Each phase is depicted by a rectangle, with the information flow being represented by arrows. able accumulations detectable in post-mortem tissues that tend to present non-specific immunolabeling across all channels, and therefore need to be identified and excluded from the analysis. Regarding the dataset's content and acquisition method, spinal cord post-mortem samples were collected and processed as described in [PERSON] et al. (1992). Specifically, the samples were immunostained for a nuclear marker (DAPI), a motor neuron specific cytoplasmic marker (ChAT) and two ALS-related proteins, Fused in Sarcoma (FUS) and Splicing Factor Proline and Glutamine Rich (SFPQ), generating four channels per image. In total, five-hundred fifty-nine images were taken with a confocal microscope using z-stacks to capture the cell's volume, and maximum intensity projections were generated. As for the analysis pipeline in [PERSON] et al. (2011), ImageJ was used for contrast enhancement and background subtraction; ilastik (2014) was used for segmentation of DAPI-positive cells; and CellProfiler (2016) was used for the final image processing, identification of nuclei using DAPI and object analysis. An example of the information contained in the four channels of the ALS image dataset is presented in Figure 3 for one of the images (Image 1 TG1B1). Images are referenced using the internal code presented in [PERSON] et al. (2011) for the sake of maintaining the same notation. The semi-supervised image analysis pipeline developed by [PERSON] et al. (2011) represents a huge improvement on standard practice, but it still requires a human operator to develop the training for the ilastik machine learning tool ([PERSON] et al., 2011), which incurs an element of variability and still requires a significant time investment. Therefore, in order to make the segmentation and identification of cells of interest completely unsupervised, an instantiation of the USRL schema was developed. Since hand labelling in multi-class segmentation requires a huge amount of time, for the present work, we selected a subset of 90 representative images from the original ALS dataset. Each of these images was hand labelled by an expert, so as to generate hand-made, multi-class segmentations. These segmentations were used as the ground truth in the evaluation of the performance of ilastik and the USRL schema. In the remainder of this work, we refer to this dataset as the ALS-GT dataset, including the 90 tissue images and their hand-made segmentations.