# Robotic Manipulation of Cumulus-Oocyte Complexes for Cumulus Cell Removal
## 3 Methods  
### Assessment of Oocyte Maturity  
In clinical operation, embryologists assess an oocyte's maturity by observing the presence of the polar body after the removal of cumulus cells. If the polar body is not observed, the oocyte will be further cultured for another 24 hours. Immature oocytes after further culturing are not qualified for ICSI and will be discarded. Recent studies have shown that communication between cumulus cells and the oocyte is essential for oocyte growth and maturation [24]. Therefore, removing cumulus cells before an oocyte is fully mature can affect its maturation process and eventually lead to maturation failure.  
An EPSANet50 network [25] is proposed to assess the maturity of the oocyte from the COC image, avoiding cumulus cell removal from an immature oocyte. As shown in Figure 3a, the proposed EPSANet50 network consists of 16 EPSANet blocks and one output module. In each EPSANet block, a pyramid squeeze attention (PSA) module [26] (see Figure 3b) was utilized to extract multi-scale features and the weighted channel attention, which can help  
Figure 2: (a) System setup. (b) Workflow of automated cumulus cell removal.  
improve the extraction ability of COC features and focus on key features such as the polar body. The high-level feature map \(F_{i}\) obtained from the \(i\)-th EPSANet block is  
\[F_{i}=Conv_{(1\times 1,1)}(Y_{i})=Conv_{(1\times 1,1)}(S_{i}\otimes Softmax(Seweight (S_{i}))),i=1,2\cdots,16 \tag{1}\]  
where \(Conv\) is the convolution operation, \(Y_{i}\) is the output feature map of the PSA module, \(\otimes\) is the multiplication in the channel dimension, \(Softmax\) is the activation function for the output part, \(Seweight\) is the \(Seweight\) module used to extract the weighted channel attention, and the input feature map \(S_{i}\) of the \(Seweight\) module is  
\[S_{i}=Conv_{(3\times 3,2)}(X_{i})\oplus Conv_{(5\times 5,4)}(X_{i}) \tag{2}\]  
where \(\oplus\) is the splicing in channel dimension, and \(X_{i}\) is the input feature map of the EPSANet block. Substituting Equation (2) into Equation (1), the high-level feature map \(F_{i}\) of the COC is  
\[F_{i}=Conv_{(1\times 1,1)}(PSA(X_{i})),i=1,2\cdots,16 \tag{3}\]  
where  
\[PSA(X_{i})=\Big{(}Conv_{(3\times 3,2)}(X_{i})\oplus Conv_{(5\times 5,4)}(X_{i}) \Big{)}\otimes Softmax\Big{(}Seweight\Big{(}Conv_{(3\times 3,2)}(X_{i}) \oplus Conv_{(5\times 5,4)}(X_{i})\Big{)}\Big{)}\]  
In the output module, the mean pixel value of the highest-level feature map \(F_{i}\) (where \(i=16\)) is calculated by the average pooling operation \(Average(\cdot)\) followed by the fully  
Figure 3: (**a**) The structure of the EPSANet50 network for assessing the maturity of an oocyte. \(X_{i}\) is the \(i\)-th (i = 1, 2, \(\cdots\), 16) input feature map, \(F_{i}\) is the high-level feature map obtained from the EPSANet block, and \(F_{16}\) is the highest-level feature map. (**b**) The structure of the PSA module for extracting more fine-grained features at different scales based on channel attention. \(w\), \(h\), and \(c\) are the width, height, and channel of the feature map, respectively. \(Conv(3\times 3\), 2) is the convolution operation (kernel size: \(3\times 3\), group size: 2), \(Conv(5\times 5\), 4) is the convolution operation (kernel size: \(5\times 5\), group size: 4).  
connected operation \(F_{c}\). Then, the _Softmax_ activation function \(Softmax(\cdot)\) is used to predict the probability C of the oocyte maturity as  
\[C=Softmax(F_{c}(Average(F_{i=16})))=Softmax\Big{(}F_{c}\Big{(}Average\Big{(}Conv_{ (1\times 1,1)}(PSA(X_{i=16}))\Big{)}\Big{)}\Big{)} \tag{4}\]