# Unsupervised Cell Segmentation and Labelling in Neural Tissue Images
## 1 Introduction
The development of reliable intelligent image processing techniques for neuronal analysis is far from trivial and is, often, heavily dependent on the image characteristics, which can be altered by factors including the biological configuration of the populations or the physical setup in which the images are acquired. However, the main problem in designing these techniques lies in the modelling of the understanding of human operators, as it is critical to replicate their reasoning into performant algorithms. A straightforward solution to overcome this problem is the use of training-based algorithms, which themselves learn human behaviour by analysing the output such humans produce. In addition, gathering training data is sometimes relatively complex and definitely time consuming, since it often involves human experts hand labelling a set of images [17]. Another notable problem is that the majority of the currently implemented systems are at best semi-supervised, needing an initial operator input, which introduces potential bias and variability among laboratories and among experimental datasets. Moreover, as of today, many of the training-based image processing methods rely on deep learning methods, such as convolutional networks [18], or other high-complexity schemes, as ensemble-based random forests [19]. Although deep learning methods might achieve good performance, the barriers in understanding how the methods work, or why errors are committed, significantly hamper both their usability and the confidence of the experts in the field [20]. Even if the experts have control over the selection of the method or the training data provided, the impossibility of auditing the result of training/learning makes the process completely opaque. That is, it remains often unclear for experts why decisions were made or how specific quantifications were computed. One solution to these issues, which would allow adaptation to different scenarios, is transfer learning. Transfer learning allows storing some learned knowledge from a specific task or data and then applying it to new scenarios [21]. However, even in such cases, the transfer's success is entirely dependent on the validity of the training data in scenarios other than the one at which it was initially aimed. In order to avoid the disadvantages of machine learning-based methods, this work proposes a novel schema that is based on three pillars: (a) removing the need for training data, therefore reducing the workload of experts; (b) increasing the ability to audit the schema, in order to provide experts with interpretable information on how decisions were made; (c) and increasing the ability to reproduce the experiments, so as to avoid problems in its transference to different scenarios. Furthermore, the state-of-the-art dataset and analysis pipeline recently developed by [PERSON] et al. [11] were used as a starting point due to their quality and high precision in both cellular and morphological feature identification. By using the same image dataset, it was possible to compare our schema to a current-generation state-of-the-art system that employs widely available machine learning tools for biological image analysis. Specifically, we designed a fully unsupervised, multi-phase schema; this approach vows to produce a more interpretable output at each step of the process, allowing experts to audit the entire process and all decisions being made. Furthermore, this schema provides the experts with an easy, interpretable configuration of the parameters that will allow applying our schema to different scenarios with little effort. Finally, our proposal is also faster than most supervised machine learning algorithms, since it does not require the labelling of training data, reducing the workload of experts significantly. The remainder of this paper is organised as follows. Section 2 unfolds the proposed schema.