Comparison of UNet, ENet, and BoxENet for Segmentation of Mast Cells in Scans of Histological Slices
This addresses the need for faster segmentation in biomedical imaging, though it is incremental as it compares and modifies existing architectures.
The study compared UNet, ENet, and a modified BoxENet for segmenting mast cells in histological scans, finding that ENet was only 1-2% less accurate than UNet but 8-15 times faster.
Deep neural networks show high accuracy in theproblem of semantic and instance segmentation of biomedicaldata. However, this approach is computationally expensive. Thecomputational cost may be reduced with network simplificationafter training or choosing the proper architecture, which providessegmentation with less accuracy but does it much faster. In thepresent study, we analyzed the accuracy and performance ofUNet and ENet architectures for the problem of semantic imagesegmentation. In addition, we investigated the ENet architecture by replacing of some convolution layers with box-convolutionlayers. The analysis performed on the original dataset consisted of histology slices with mast cells. These cells provide a region forsegmentation with different types of borders, which vary fromclearly visible to ragged. ENet was less accurate than UNet byonly about 1-2%, but ENet performance was 8-15 times faster than UNet one.