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The Detection of Hela Cells in Brightfield Images

Among all the rapidly developing pathological diagnosis methods, particular cell therapy has become one of the most popular research tasks. In some occasions of cell detection and segmentation, several methods of separating presented touching and overlapping cell structures need to be utilized. Applying and developing these methods has become one of the most crucial and error-prone tasks in further analysis of brightfield images. In this work, we choose HeLa cells in a specific cell tracking dataset to detect HeLa cells in brightfield images and describe an approach to do cell detection and further analysis. Given a set of brightfield HeLa cell images in the cell cycle, we separate them into the border, centre, and blank sessions as the labels. Patches are extracted from images after binarization. When they are distinguished and labelled, we utilize different filters as pre-process labels and carry on data augmentation to obtain abundant patches as our training dataset. We find that SVM is a desirable model for classification since it performs well in most datasets, and LeNet, which is able to respond to a part of the surrounding units, can also be applied in our experiment. Therefore, we prefer SVM and LeNet as our models to do classification and prediction. In optical microscopy, especially when transmitted light and fluorescence microscopy are related to the specific cell structure segmentation, the distinct approach that we introduced in this work about separating touching and overlapping cell structures represents a desirable performance with high efficiency and robustness