| Summary: | Cell segmentation is challenging owing to the existence of various experimental configurations, cell shapes that cannot be mathematically defined, and ambiguous cell boundaries. We propose a cell segmentation method using cell region discriminator and multi-cell discriminator trained using heterogeneous machine-learning techniques such as logistic regression, expectation-maximization, and support vector machine (SVM). The cell-region discriminator identifies the regions where cells are found in images obtained from microscopes via a secondary logistic regressor, and its features use statistical information as well as the distribution of neighbor intensities. The SVM-based multi-cell discriminator determines whether multiple cells are present in the region detected by the cell-region discriminator and whether the region should be divided using the expectation-maximization algorithm. We suggest features for the boundary sectional area and the least square error for cell surface fitting to train the multi-cell discriminator. Using the features and the SVM, the multi-cell discriminator can be trained without overfitting, even for small training data. During this process, the proposed convex cell surface enhances the clustering performance. In experiments, our method based on two discriminators stably divided connected cells even when the contrast between a cell and the background area was small, and it outperformed state-of-the-art methods in terms of cell detection and segmentation accuracy.
|
|---|
