# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 1 Introduction  
Computational neuroscience has made impressive progress in the modelling of the human brain. Most of the relevant findings have been made studying animal brains [1], which share common properties with the human brain. For decades, neuroscientists used many brain imaging methods for this purpose. Some of the most commonly used are electroencephalography (EEG) [2], positron emission tomography (PET) [3], magnetic resonance imaging (MRI) [4] and functional magnetic resonance imaging (fMRI) [5]. These methods have specific characteristics that make them suitable for certain applications, but they lack two relevant properties: real-time monitoring of neural circuits and single cell resolution. In 2003, [PERSON] et al. [6] proposed a protocol based on two-photon in vivo calcium imaging that achieved individual cell resolution and real-time analysis of brain tissues in mice. Two-photon calcium imaging opened a multitude of research paths in neuroscience, such as in the study of visual cortex [7, 8], the olfactory bulb [9] or the cerebellum [10]. Nevertheless, the large scale acquisition of images to be segmented at an individual cell level poses the need for automated neuron segmentation. Two-photon calcium imaging produces 3D volumes where each image plane records a specific located brain region (typically 10-20 um) at a given time. Neuron spikes are produced at different temporal moments, resulting in large stacks of data where neurons are identified. In addition, neurons may appear overlapped on a single frame, and usually the intensity of the spikes varies significantly, a large portion of the neurons being invisible in most of the time frames. Precise annotation requires taking into account the full stack of spatio-temporal data. Manual annotation of these data requires a cumbersome effort from expert neuroscientists. The process of manually drawing binary masks on each temporal frame is not scalable, and usually two independent annotators can use different criteria on the sametask. A logical step is to let computers learn this task from previously annotated samples, producing reproducible results across research labs. The pipelines for neural data analysis are generally comprised of three steps apart from the preprocessing. The first step focuses on identifying and segmenting all the neurons visible in the field of view. The second step is aimed at extracting temporal traces of the identified neurons and the third step is aimed at spike deconvolution i.e., inferring the time and frequency of neural spikes. This pipeline is illustrated in Figure 1.  
The number of publications dealing with calcium imaging segmentation has increased significantly. The main motivation of this survey is to provide a comprehensive overview of the recent state-of-the-art works and a proper classification of these methods for its use in neuroscience research. We provide a brief summary of the main features of each method, and a succinct description of their segmentation performance on public benchmarks. This evidence may be useful for neuroscientists that seek out-of-the-box solutions for their particular problems. This survey reviews some of the most relevant papers on automated neuron segmentation in two-photon calcium imaging (first step of the pipeline illustrated in Figure 1). First, we introduce a taxonomy of the main methods analysed and describe the neural segmentation pipeline. Then we highlight the specific contributions that had an impact in the field. The two main challenges to boost the segmentation are the availability of open code for comparing and improving the current methods and the publication of benchmarks with manually annotated ground truth that allows a fair comparison among  
Figure 1: General pipeline followed for calcium imaging data analysis. (**a**) A summary image (see Appendix A.3 for details) representation of a calcium imaging stack. (**b**) Neuron detection/identification/segmentation step where the identified neurons are highlighted.