“Deep Learning-Based Automated Segmentation of White Blood Cells in Microscopy Images: A Survey

In this section, the authors discuss the relevant funding agencies that have supported their research on image segmentation for medical imaging. They explain how they used the K-Means clustering method in the second stage of image processing to separate regions of interest, such as cytoplasm, background, and nucleus. The authors also describe the watershed algorithm used in the final stage to obtain instance segmentation, which helps identify individual cells.
The authors emphasize that each stage is crucial for accurately identifying and separating different components within medical images. They use everyday language and metaphors to explain complex concepts, making it easier for readers to understand the article’s key points without oversimplifying the information.