MMUNet: Morphological feature enhancement network for colon cancer segmentation in pathological images

Colon image analysis is an important step in diagnosing colon cancer, and achieving automated and accurate segmentation remains a challenging problem because of the diversity of cell shapes and boundaries in pathological sections. In this paper, we propose a U-shaped colon cancer segmentation network, which combines depth-separable convolution and morphological methods to reduce the number of model parameters and effectively improve segmentation accuracy. We improve the global and local feature capabilities by taking advantage of serial convolution and external focus as the underlying architecture for the model. We designed the skip connection to fuse the features from the encoder in a morphological way to enhance the morphological features. We introduced an edge enhancement module by extracting contour information using morphological methods to enhance edge features. We evaluated the proposed method on three colon cancer datasets, and the experimental results showed that our method with a small number of parameters has a Dice coefficient of 92.76% ± 5.86% on the Glas dataset, 86.11% ± 7.11% on the CoCaHis dataset, and 91.61% ± 11.25% on the Colon dataset. The code will be openly available at https://github.com/Yuanhaojun513/MMUNet.