# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 3 Methods
### Detection of Cumulus Cells  
Since cumulus cells are small and appear blurred due to out-of-focus imaging, it is difficult to determine the number and radius of cumulus cells within a COC. Previous studies used binary classification networks that qualitatively assess the cumulus cells surrounding the oocyte. However, these networks are unable to accurately detect the  
Figure 5: Diagram of the adaptive control system. number of cumulus cells and show relatively large classification errors when a few small and blurry cumulus cells remain on the oocyte [22; 23]. In this study, an improved Yolov5s network is proposed to accurately detect cumulus cells for the quantitative assessment. As shown in Figure 6, the improved Yolov5s network consists of three major parts: the backbone, neck, and head. The features of the COC are first extracted by the backbone. Then, the extracted features are further fused by the neck. Finally, the head processes the result of feature fusion and outputs the detected cumulus cells with rectangular bounding boxes. The cumulus cell removal rate \(r\) is defined as  
\[r=\frac{R_{Before}-R_{After}}{R_{Before}} \tag{12}\]  
where \(R_{Before}\) is the number of cumulus cells detected before removal, and \(R_{After}\) is the number detected after removal. \(r\) ranges from 0 to 1, and \(r=1\) means the complete removal of cumulus cells. Compared with the traditional Yolov5s [29], several improvements have been made in the proposed Yolov5s to increase the accuracy of cumulus cell detection as follows. In the backbone, the traditional Yolov5s uses a CSPDarknet53 module [30] to repeatedly calculate the gradient between two connecting neuronal nodes for backpropagation, which reduces the network's capability of feature expressiveness and extraction. To overcome this limitation, the CSPDarknet53 module is replaced by the Res2 Net-COT module [31] in the improved Yolov5s, as shown in Figure 7a. The input COC feature map is divided into several sub-feature maps \(X_{i}\), which is followed by feature transformation \(K_{i}\) and feature fusion \(Y_{i}\). The feature fusion \(Y_{i}\) is defined as  
\[Y_{i}=\left\{ \begin{aligned} X_{i},i=1\\ K_{i}(X_{i}+Y_{i-1}),1<i<s\end{aligned} \right. \tag{13}\]  
where \(s\) is the number of sub-feature maps. This structure preserves multi-scale feature information and increases the receptive field, which helps alleviate the problem of gradient repetition and enhances the semantic expressiveness of cumulus cell features. Additionally, the COT block embedded within the Res2 Net-COT module captures richer contextual information and extracts more useful cumulus cell features, leading to improved detection accuracy. Figure 6: The structure of the improved Yolov5s network for detecting cumulus cells. In the neck, the FPN-PAN module [32] of the traditional Yolov5s has several limitations, such as lower feature utilization and excessive redundancy, which lead to insufficient feature fusion and incomplete feature transmission. To overcome these issues, the BiFPN module [33] is introduced to replace the FPN-PAN module, as shown in Figure 7b. The BiFPN module constructs a bidirectional feature pyramid structure by employing both top-down and bottom-up paths, enhancing the fusion of cumulus cell features at different scales and improving the feature transmission capability. In the head, the CBS module [34] used in the traditional Yolov5s adopts a serial transmission structure. This structure causes the feature maps of the three detection branches to have high latitude characteristics, increasing the model computation and thus reducing the inference speed of the model. In the improved Yolov5s, the CBS module with a serial transmission structure is replaced by the inception module [35] with a parallel transmission structure, as shown in Figure 7c. The COC feature map \(M_{i}\) is first input into a parallel structure with two branches.