# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 4 Signal/Image Processing-Based Methods
The authors have tested their algorithm and routines on a synthetic dataset and an in vitro calcium imaging dataset. The synthetic dataset was generated by creatinga neurons firing sequence and adding this to a baseline fluorescence at known locations, at different amplitudes and with different neurons sizes. An image stack of 1200 frames was created using this method. The other dataset was acquired from neuron activity at localized sites in human neuroblastoma SH-SY5Y cells cultured in vitro. The image stack was acquired at 420 frames per second. The ground truth was created by visually inspecting the stack and localizing neural activity. The authors report a maximum false positive rate of 0.007 events per frame (one false positive per cell every 3 s). The authors have not reported the performance of their toolbox on benchmarks, therefore it is hard to evaluate the robustness of their toolbox on diverse datasets. Moreover, the calcium events localization routine in their toolbox seems to ignore the occurrence of overlapping neurons, which can significantly drive the performance down. [PERSON] et al. developed an open-source bioinformatics tool for automatic extraction, counting and localization of calcium signals in two-photon calcium imaging data [52]. They specifically targeted calcium signals whose amplitude is close to background fluorescence levels. The tool interactively asks the users to enter three parameters: grid window size, signal to noise ratio (SNR) and signal average threshold (SAT). The SNR value defines the threshold separating calcium events from noise and SAT value is used to separate signal from the background. First, the input image stack is divided into multiple grid cells. Then, average calcium fluorescence is calculated for the grid window and its amplitude is compared with the SAT value. Wavelet transform is used to localize the peak in the calcium signal. It is based on the assumption that calcium signal has three components, the peak (calcium transient event), the baseline and the noise. The authors have tested their tool on hippocampal neurons prepared from CDI mice of either sex. The authors reported an average true positive rate of 93% and an average false positive rate of 2.5%. They have not reported any false negative rate. Moreover, the tool has not been tested on a benchmark dataset, so we cannot establish its robustness. Additionally, temporal information is only used once the neurons are detected (grid cell containing neurons); therefore, this tool cannot effectively deal with spatially overlapping neurons. [PERSON] et al. proposed an adaptive thresholding-based cell segmentation approach which they call \"ACSAT (Automatic Cell Segmentation by Adaptive Thresholding)\" ([53]). This approach is optimized for cell segmentation in large datasets. First, they collapse the 3D calcium imaging stack onto a 2D image by taking a temporal average. In the second step, ACSAT detects ROIs at various threshold iteratively. In the first iteration, an initial threshold is used to segment potential ROIs from the background. The locations of these ROIs is saved and then the ROIs are eliminated. Next, the threshold is lowered for next iteration. The thresholding is applied both at global level and at local level. The global thresholding segments the ROIs from the background whole the local thresholding separates overlapping ROIs. ACSAT has been validated on a simulated dataset with a recall generally higher than 80%, however, the authors report that the precision and recall fall if the SNR is below 20 dB. ACSAT has also been validated on an actual dataset acquired from the hippocampus of a mouse. It achieved a precision of 71.2% and a recall of 74.9%. On another dataset acquired from the striatum, it achieved a precision of 51.1% and a recall of 75.8%. The performance figures of ACSAT are decent and it is simple enough to be understood and computationally not very expensive. However, it cannot deal with those neurons whose temporal activity is sparse and might not register strongly in the mean image.