# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 1 Introduction  
Neurodegeneration is a broad concept that generally refers to any pathology that features the loss of neuronal populations [1]. Although different Neurodegenerative Diseases (NDs) are caused by various molecular events and present distinct clinical features, they often share a significant number of pathological hallmarks. Therefore, research strategies and tools used to study NDs are often similar. Relevant examples of NDs are Alzheimer's disease [2], Amyotrophic Lateral Sclerosis (ALS) [3] or Parkinson's disease [4], where different neuronal populations are selectively affected, but present shared molecular features that can be investigated using the same tools. Over the past few years, with the increase in life expectancy, the relevance of NDs has increased significantly [5], boostedby the fact that the vast majority of these pathologies are incurable and with limited therapeutic options. Research on NDs involves different strategies and disciplines, and as a result, it has evolved into a broad interdisciplinary field focused on efforts to understand, prevent and alleviate the molecular effects of neurodegeneration. Therefore, a significant part of this effort is devoted to understanding the molecular mechanisms of neurodegeneration at the single-cell level, both in the form of observational experiments to identify specific molecular phenotypes (e.g., imaging studies on affected vs. unaffected neurons to assess mislocalisation of key proteins) and by follow-up interventional hypothesis-testing studies (e.g., treatment with potential therapeutic molecules to alter the observed disease phenotype). The vast majority of these studies are based on microscopy observation of the neuronal population affected, either in vitro within cell culture models or from ex vivo or post-mortem samples. As microscopy technology advances, widespread adoptions of high-content screening [6; 7] and imaging with automated microscopy [8] have revolutionised the way of using imaging-based analysis, especially for neurodegeneration molecular phenotyping either in vitro [9; 10] or ex vivo [11; 12]. As a result, researchers have increased throughput and analytical power in their studies, as the imaging systems can automatically acquire thousands of images. Additionally, the possibility of increasing the availability and size of imaging datasets has been instrumental also for molecular pathology laboratories dealing directly with biopsies from patients or post-mortem samples. However, as an associated outcome, the bottleneck has been shifted towards analysing these large datasets where information needs to be gathered, potentially at a single-cell level, benefiting the in-depth analysis of heterogeneous samples. In many laboratories, the analysis of these datasets has as its main prerequisite the necessity of correctly identifying the neurons (or other cell types). Furthermore, it is often relevant to further segment the cells into their constituent parts (e.g., nucleus, axons, cell bodies, etc.) to perform more detailed molecular analyses, like protein localisation [11]. Moreover, other experiments might require the extraction of more precise data from the images at a population level (for example, the neural network) or even viability or mitosis rates over a certain period of time [13]. In the data extraction context, identifying target cells and the segmentation of morphological features are some of the most crucial phases and are often performed either manually or in a semi-supervised way, leading to an increase in human workload. Moreover, this phase can be seen as both a goal itself or as a pre-processing step in image-associated molecular studies. The literature contains a vast number of methods that are fully or partially aimed at object segmentation in ND-related imaging [14; 15; 16].