# How to Use the Osteoclast Identifier Software
## 1 Introduction
### Alternative Software for Cell Sorting and Identification
#### 1.2.3 Cellpose  
Cellpose is a cell segmentation software designed for biological microscopy images, particularly for fluorescence microscopy and brightfield microscopy. Additionally, Cellpose can be used as a plugin for ImageJ, allowing users to perform cell segmentation directly within the ImageJ environment. Cellpose excels at segmenting various types of cells in both 2D and 3D images. When analyzing cells with similar morphological characteristics, Cellpose demonstrates exceptional performance. However, due to algorithmic constraints, Cellpose primarily extracts areas, perimeters, and roundness as key features during AI analysis for image segmentation. Irregularly shaped osteoclasts are inherently difficult to classify accurately based solely on these features.  
Moreover, Cellpose also employs segmentation (similar to OC_Finder), which is better suited to the morphological analysis of cells. When the primary goal is cell classification  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Software** & & **Advantages** & **Limitations** \\ \hline \multirow{6}{*}{OC\_Identifier} & - & Specialized in osteoclast identification, & \multirow{2}{*}{-} & \multirow{2}{*}{-} \\  & & particularly suited for analyzing & & \\  & & osteoclast development. & & \\  & - & Provides a user-friendly GUI, making it & & \\  & - & accessible for researchers. & & \\  & - & Allows further training on user-provided & & \\  & & experimental data for & & \\  & & optimized performance. & & \\ \hline \multirow{6}{*}{Image} & - & Open-source software widely used in & - & Primarily relies on manual or \\  & & biomedical image processing. & & semi-automated operations, with lower \\  & - & Offers a large number of plugins for & & automation levels. \\  & - & extended functionality. & - & Lacks built-in deep learning capabilities; \\  & - & Suitable for general tasks such as cell & & requires additional plugins for \\  & - & counting and morphological analysis. & & AI-based analysis. \\ \hline \multirow{6}{*}{Cellpose} & - & Supports a wide range of cell types using a & \multirow{2}{*}{-} & \multirow{2}{*}{Primarily focused on cell segmentation} \\  & & general neural network for cell segmentation. & & \\  & - & Pre-trained models are available, eliminating & & rather than specific cell-type identification. \\  & - & the need for additional training. & - & Users may need to adjust parameters for \\  & - & Robust performance across different types of & & higher accuracy. \\  & - & microscopy images. & & \\ \hline \multirow{6}{*}{DeepCell} & - & Applicable to various cell types and based on & - & Requires internet access, as some features \\  & - & deep learning cell segmentation. & & depend on cloud-based processing. \\ \cline{1-1}  & - & Provides cloud-based computation, making & - & High computational resource requirements, \\ \cline{1-1}  & - & it suitable for large-scale data analysis. & & making it less suitable for environments with \\ \cline{1-1}  & - & Useful for analyzing complex tissue sections. & & limited local computing power. \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of advantages and limitations of OC_Identifier, ImageJ, Cellpose, and DeepCell.  
and counting, object detection (as used in OC_Identifier) operates at a significantly faster speed. Furthermore, Cellpose does not inherently display specific cell names or perform proportion calculations. Users must modify and adjust these functionalities at the code level. As a result, it has a certain threshold for use.