Using Multiclass Semantic Segmentation for Close Boundaries and Overlapping Blood Cells

In this paper, we present a method to solve cells overlap and close boundaries problem during blood cells images segmentation with deep learning techniques. The CNN encoder-decoder based models have been developed recently to apply various segmentation tasks, however, loss of essential aspects in the data is a common issue during the model training, especially when it comes to low level features. Our work aims to fix this issue by dedicating a new class for cell boundaries and then segmenting them using U-Net. This method is helpful in complex imaging environment where the objects have close boundaries or overlap one over the other which makes it competitive for the model to recognize the corresponding segmentation contour.