# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 7 Summary of Performance on Public Benchmarks, Applications and Recommendations
### Applications and Recommendations  
This survey article is intended to function as a set of insights and guidelines to the researchers who want to work with calcium imaging, either in vivo or in vitro. It can be used as a quick preview of the literature on neuron segmentation/neural activity detection in calcium imaging. Additionally, it can be used as a guideline for which method to choose for neuron segmentation in case the researcher gets their hands on a calcium imaging dataset. This survey can be used a guiding set of principle by researchers in the following fields. * Neuroscientists working on developing semantic neuronal networks from calcium imaging data recorded at sub-cellular resolutions. * Neuroscientists working to understand the behaviour of neurons affected by some neurodegenerative diseases. * Researchers working on understanding the firing patterns of neurons as a response to some physical stimuli. * Researchers working to understand neurons behaviour in detail and then build in silico model of these neurons which in turn can be used to develop neural networks which mimic the behaviour of their biological counterparts. * Last but not the least, researchers working on neuromorphic computing who want to understand the operations of neuronal networks at sub-cellular resolutions. Based on the computational requirements, complexity and ease of use, we have following recommendations. * Signal/image processing-based methods
* Signal/image processing-based approaches are usually not complex and computationally expensive. A little background in fundamental signal/image processing is enough to get the gist of these methods. * In order to research the effect of various parameters on the outcome of these methods, the researchers need to know all the related signal/image processing concepts in detail. * For those researchers who are not from a computational background (neuroscientists), the pipeline if these methods is easy to follow and can be used a set of tools with few parameters to tweak. * These methods are ideal for neuron segmentation in in vitro datasets since these datasets do not have the overlapping neurons problem as much as the in vivo datasets. * These methods tend to be not computationally as expensive, which is why the are also recommended if computational power is an issue. Moreover, without investing a lot on expensive computational hardware, these methods (or at least some of them) can be easily deployed for real-time or online inference. * Matrix factorization-based methods
* The base framework of these methods is a bit harder to grasp and can be computationally expensive. * For those researchers who are not from a computer science background, these methods might pose a challenge since the success/failure of these methods depend heavily on the choice of factorization framework, background model and baseline fluorescence model. * These methods have a modest performance on in vivo datasets as well, so they can be used for neuron segmentation in in vivo datasets with acceptable performance. * Since these methods tend to be computationally expensive, they are not recommended if computational cost is an issue. Due to this specific issue, most of these methods are not recommended for real-time or online inference. For instance, we could find only one approach [69] in this category which can be deployed online. * Machine learning-based methodsThe analytical model behind ML/DL approaches is complex, but ML/DL methods are usually treated as black box models, so if the method is end-to-end (provide data and get a segmentation mask), then these methods are easier to grasp from a holistic perspective. * These methods perform fairly well on in vivo datasets as well, especially when they are trained on raw calcium imaging stacks and not just on summary images. * If computational cost is not an issue, then these methods are recommended. * DL-based methods are always data hungry, so if annotated data availability is an issue, then these methods are not recommended.