# Computational Methods for Neuron Segmentation in Two-Photon Calcium Imaging Data: A Survey
## 7 Summary of Performance on Public Benchmarks, Applications and Recommendations
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Type** & **Code Availability** & **Performance** & **DB** & **Complexity** \\ \hline [PERSON] et al. [44] Signal and image processing-based & MATLAB code available at [[https://bit.ly/2](https://bit.ly/2) GItq4O]([https://bit.ly/2](https://bit.ly/2) GItq4O) & Precision = 89.3\%, F1 = 88.1\% & MC & - \\ \hline [PERSON] et al. [53] Signal and image processing-based & MATLAB code available at [[https://bit.ly/328n2](https://bit.ly/328n2) Lx]([https://bit.ly/328n2](https://bit.ly/328n2) Lx) & Precision = 71.2\%, F1 = 72.3\% & MC & - \\ \hline [PERSON] et al. [43] Signal and image processing-based & Python code available at [[https://bit.ly/36](https://bit.ly/36) qtJw3]([https://bit.ly/36](https://bit.ly/36) qtJw3) & Precision = 65.8\%, F1 = 59.6\% & NF & \(O(WHD)+O((WH)^{2})\) \\ \hline Reynolds and image processing-based & MATLAB code available at [[https://bit.ly/3](https://bit.ly/3) lb776R]([https://bit.ly/3](https://bit.ly/3) lb776R) & F1 = 67.5\% & NF & \(O(WHK)\) where K is the dimension of the levelset \\ \hline [PERSON] et al. [69] & Python code available at [[https://bit.ly/2](https://bit.ly/2) SzbJHW]([https://bit.ly/2](https://bit.ly/2) SzbJHW) & Precision = 87\%, F1 = 79\% & MC & RT \\ \hline [PERSON] et al. [56] & MATLAB code available at [[https://bit.ly/36](https://bit.ly/36) PuoaB]([https://bit.ly/36](https://bit.ly/36) PuoaB) & F1 = 75\% & MC & \(O((WHD)^{2}Nm)\) \\ \hline [PERSON] et al. [97] Matrix factorization-based & Python code available at [[https://bit.ly/3g5](https://bit.ly/3g5) vvT]([https://bit.ly/3g5](https://bit.ly/3g5) vvT) & Precision = 57.8\%, F1 = 55\% & NF & - \\ \hline [PERSON] et al. [98] Matrix factorization-based & Python code available at [[https://bit.ly/3](https://bit.ly/3) dSPmai]([https://bit.ly/3](https://bit.ly/3) dSPmai) & Precision = 59.9\%, F1 = 58.3\% & NF & - \\ \hline [PERSON] et al. [84] Machine learning-based & Python code available at [[https://bit.ly/30](https://bit.ly/30) Ky49C]([https://bit.ly/30](https://bit.ly/30) Ky49C) & F1 = 71\% & MC & O(4K+2F) \\ \hline [PERSON] et al. [76] Machine learning-based & MATLAB code available at [[https://bit.ly/3](https://bit.ly/3) iEJEcA]([https://bit.ly/3](https://bit.ly/3) iEJEcA) & F1 = 85\% & MC & - \\ \hline [PERSON] et al. [43]+[PERSON] et al. [99] & Python code available at [[https://bit.ly/33](https://bit.ly/33) EVzTc+]([https://bit.ly/33](https://bit.ly/33) EVzTc+) Recall = 60.2\%, F1 = 61.7\% & NF & O(6K+F) \\ \hline [PERSON] et al. [78] Machine learning-based & Python code available at [[https://bit.ly/3d60](https://bit.ly/3d60) HTG]([https://bit.ly/3d60](https://bit.ly/3d60) HTG) & Precision = 70.2\%, F1 = 56.9\% & NF & O(9K) U-Net 2 Ds \\ \hline [PERSON] et al. [21] & Python code available at [[https://bit.ly/36](https://bit.ly/36) pvvNL]([https://bit.ly/36](https://bit.ly/36) pvvNL) & F1 = 69\% & NF & O(9K) U-Net \\ \hline [PERSON] et al. [100] + [94] & Python code available at [[https://bit.ly/30G96](https://bit.ly/30G96) YU]([https://bit.ly/30G96](https://bit.ly/30G96) YU) & F1 = 67\% & NF & O(3K+F) CNN +O(5F) \\ \hline [PERSON] et al. [90] & Python \& MATLAB code available at & F1 = 67.5\% & NF & O(6K+F) \\ \hline [PERSON] et al. [79] Machine learning-based & Python \& Python code & & & \\ \hline [PERSON] et al. [79] Machine learning-based & available at [[https://bit.ly/3](https://bit.ly/3) wEjsaU]([https://bit.ly/3](https://bit.ly/3) wEjsaU) & F1 = 85\% & NF & - \\ \hline [PERSON] et al. [95] Machine learning-based & Python \& Python code & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different approaches. For each method, we provide the reference, a link to the code (if available, accessed on 2 July 2022), the key performance indicators reported (if available), the databases used (MC stands for manually curated and NF stands for the Neurofinder dataset) and the approximate complexity order at testing time.