# Advanced Biological Imaging for Intracellular Micromanipulation: Methods and Applications
## 3 Intracellular Micromanipulation with Computational Image Information
### 3D Analysis  
The aforementioned approaches either adjust manipulation tools or rotate targeted objects into a predefined height, so they seem applicable to large specimens, such as embryos and oocytes. For small organelles with irregular shapes and random spatial distribution, an operation plane should be selected through a deliberate 3D analysis of sampled images. In an active snake node method proposed to generate closed biomembrane contours, a 3D model of an egg cell was reconstructed using third-degree nonuniform rational B-splines to inspect the micropipette insertion [159] and measure the force [160], as shown in Figure 10a. An ellipsoid estimation method was applied to reconstruct attached bladder cancer cells [161]. Four intersection points were detected on the upper cell surface with motion history images. However, these fitted models were derived from the assumption that targeted cells have a regular shape. Specifically [159], it was assumed that the sampled image was assumed to lies in the symmetric central plane, leading to undoubted discrepancies with reality. Another 3D morphology reconstruction was achieved on the basis of a standard alpha-shape algorithm for cell rotation measurement [162]. The cell contours in each sampled image were extracted with a threshold value. Even though contour extractions were repeated for several rounds, they may contain background noises and out-of-focus blurs. Besides, this method is not feasible for organelles because they are undistinguished from other intracellular components in WFFM-sampled images. Fluorescence labeling can filter surrounding objects, but it cannot reject imaging blurs and noises. However, optical fluorescent sampling with a CFM can be applied to reject out-of-focus blurs and reflect object morphology. Figure 10b illustrates the CFM reconstruction of endothelial cells [163] and the nucleus of fibrosarcoma cells [164]. Manipulation tools lowered down and indented the cells with a visualized distance under 3D observation. Besides, a 3D reconstruction method integrated with a noise-regulated maximum likelihood estimation (NRMLE) deconvolution and filtered segmentation algorithm has been developed for WFFM [165]. In this method, the sampled fluorescent images were deconvoluted with NRMLE for deblurring and denoising. A filtered process was initiated before segmentation to compensate for the non-uniform concentration and distribution of fluorescence molecules. The reconstructed nucleus has high accuracy and a similar structure to that observed via CFM. An appropriate operation position can be obtained through the geometric analysis of the generated model, thereby improving the accuracy and efficiency of nuclear delivery, as shown in Figure 10c. Recently, a SIM-based accurate 3D spine neurons measurement and analysis method was proposed [166]. Figure 10d shows the 3D computational reconstruction process. First, the voxel data of SIM sampled images was converted into surface mesh data, then 3D features were extracted from the spine morphology dataset, and the 3D model was finally generated from dimension transformation and shape classification. With the super-resolution ability of SIM, this method can detect head curvature changes in small-sized spines (<0.18 m\({}^{3}\)).  
Figure 10: _Cont._