<divclass="disc">Figure 6. The 0.17mm coverslip enable us to test cell under the 60x microscope.</div>
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<p>2. Design the Jig of the chip to fix the microfluidic chip and reduce the stress on the PDMS material from stainless-steel tube. Thus, the focus shift was largely reduced.
<divclass="disc">Figure 2. The YOLOv8 structure for dot matrix segmentation.</div>
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<p>The process of manually annotating the massive amount of image data obtained from microscopy is not only time-consuming but also labor-intensive. To address this, we have implemented machine learning models for the automatic recognition of binary matrix values, streamlining the batch processing workflow. Focusing on the binary nature of these matrices, we simplified the model by utilizing only the grayscale channel and applied histogram equalization to boost contrast. We utilized CNNs, renowned for their prowess in image processing, with a compact architecture consisting of two convolutional layers succeeded by two fully connected layers (Fig 3). Compared and tested on different models, the CNN is sufficient to meet our needs. To enhance the robustness of the model and reduce overfitting, we expanded the dataset by generating additional images through transformations based on the manually annotated samples. This approach significantly improves the efficiency of biological image processing using machine learning techniques with good model performance (check performance for details).
<divclass="disc">Fig 3. The CNN structure for dot recognition.</div>
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<p>Based on the latest iteration of the cellpose model, cyto3, we have trained our model to precisely recognize and segmented yeast cells [3]. In our implementation with tagged images with its number, the model is initiated with image input, and proceeded with denoising, and calibrates cell diameter pixels to optimize recognition accuracy. Then we manually modified incorrectly segmented yeast cells, thereby ensuring the precise segmentation of yeast (Fig 4).
