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：直肠癌(CRC)是发病和死亡率最高的三种恶性肿瘤类型之一。 直系病理学图像是诊断结肠癌的金标准。 细胞核核分解和分类以及核成分回归任务可以帮助分析结肠组织中的肿瘤微环境。 传统方法仍然无法同时处理两种类型的任务, 并且预测准确性和高应用成本。 本文提议基于UNet 框架( 称为 MGTUNet) 的处理核子的新 Uet 模式, 称为 MGTUNet, 使用Mish、 Group 正常化和转换变异层来改进分解模型, 以及调整流L1 LOS 值的护林优化器。 第二, 它使用不同渠道进行分解和分类, 最终完成核细胞分解和分类任务, 以及核元素回归任务。 最后, 我们用八个分解模型进行了广泛的比较实验。 通过比较三种评价指标和模型的参数大小, MGTUTUNet- GOL5 和MQ 在P 0.65 上演示了0. 0. 0. 0. 0.65 和 MQ 。