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.

翻译：结直肠癌（CRC）是发病率和死亡率均位列前三的恶性肿瘤类型。组织病理学图像是诊断结肠癌的金标准。细胞核实例分割与分类、细胞核成分回归任务有助于分析结肠组织中的肿瘤微环境。传统方法尚无法同时以端到端方式处理两类任务，且预测精度低、应用成本高。本文基于UNet框架提出一种新型细胞核处理模型MGTUNet，该模型采用Mish激活函数、组归一化（Group Normalization）与转置卷积层改进分割模型，并通过ranger优化器调整SmoothL1Loss值；其次，利用不同通道对不同类型细胞核进行分割与分类，最终同步完成细胞核实例分割与分类任务及细胞核成分回归任务。通过使用八种分割模型开展广泛对比实验，从三项评估指标及模型参数量进行对比，MGTUNet在PQ、mPQ和R²上分别取得0.6254、0.6359和0.8695的结果。实验表明，MGTUNet现已成为结肠癌组织病理学图像量化的最先进方法。