Information Added U-Net with Sharp Block for Nucleus Segmentation of Histopathology Images

Abstract

Segmenting nuclei from histopathology images is a crucial step in the early identification and diagnosis of several diseases. Due to the complexity of histopathology images, accurate nucleus segmentation is not a simple operation. However, convolutional neural networks (CNNs) have recently been revealed to be a viable option. The well-known CNN model, namely the U-Net, demonstrated its image segmentation effectiveness in medical field. However, U-Net has several drawbacks, such as information loss after transmission through particular steps. Another significant one is the likelihood of feature mismatches in the encoder and decoder sub-networks in skip connection, which can lead to the fusing of semantically unrelated information and, as a consequence, fuzzy feature maps throughout the learning process. In order to solve these issues, an improved U-Net architecture called Information Added U-Net with Sharp Block (IASB-U-Net) has been proposed for nuclei segmentation from histopathology images. Information is added to the encoder-decoder path in the proposed model after each layer, and sharpening spatial filters are utilized in place of skip connections. The experimental study over a merged dataset demonstrates that the proposed IASB-U-Net produces competitive results when compared to established CNN models such as U-Net, Dense U-Net, SCPP Net, and LiverNet.