  Colorectal cancer (CRC) is among the top three malignant tumor types in terms
of morbidity and mortality. Histopathological images are the gold standard for
diagnosing colon cancer. Cellular nuclei instance segmentation and
classification, and nuclear component regression tasks can aid in the analysis
of the tumor microenvironment in colon tissue. Traditional methods are still
unable to handle both types of tasks end-to-end at the same time, and have poor
prediction accuracy and high application costs. This paper proposes a new UNet
model for handling nuclei based on the UNet framework, called MGTUNet, which
uses Mish, Group normalization and transposed convolution layer to improve the
segmentation model, and a ranger optimizer to adjust the SmoothL1Loss values.
Secondly, it uses different channels to segment and classify different types of
nucleus, ultimately completing the nuclei instance segmentation and
classification task, and the nuclei component regression task simultaneously.
Finally, we did extensive comparison experiments using eight segmentation
models. By comparing the three evaluation metrics and the parameter sizes of
the models, MGTUNet obtained 0.6254 on PQ, 0.6359 on mPQ, and 0.8695 on R2.
Thus, the experiments demonstrated that MGTUNet is now a state-of-the-art
method for quantifying histopathological images of colon cancer.
