结直肠癌免疫组化图像分级诊断方法

Grading and Diagnostic Method for Colorectal Cancer Immunohistochemical Images

人体组织病理学检查主要应用于各类肿瘤诊断和治疗,免疫组织化学技术在结直肠癌早期筛查中具有重要的临床意义。为准确判断结直肠癌抑癌基因p53表达程度,文中提出一种基于逐块释放微调策略迁移学习的分级诊断方法,通过图像预处理、有监督模型预训练以及逐块释放微调等步骤将细胞核分割模型的参数迁移至诊断框架中。生成的细胞核分割掩膜进行主成分分析(Principal Component Analysis,PCA)降维和支持向量机(Support Vector Machine,SVM)多元分类最终得到图像诊断结果。该方法在结直肠癌p53蛋白免疫组化(Immunohistochemistry,IHC)图像数据集上进行了验证,模型的Dice值可达到0.8876,分级准确率达到90.28%。结果表明,所提方法能够对结直肠癌免疫组化图像有效分级,为医生阅片提供可靠的辅助信息。

Human tissue pathology examination is mainly used for the diagnosis and treatment of various tumors.Immunohistochemical technique has important clinical significance in the early screening of colorectal cancer.In order to accurately determine the expression level of the tumor suppressor gene p53 in colorectal cancer,this study proposes a grading diagnostic method based on transfer learning with block-wise fine-tuning strategy.The parameters of the cell nucleus segmentation model are transferred to the diagnostic framework through image preprocessing,supervised model pre-training,and block-wise fine-tuning.The generated cell nucleus segmentation mask is subjected to PCA(Principal Component Analysis)dimensionality reduction and SVM(Support Vector Machine)multivariate classification to obtain the final image diagnosis result.The proposed method is verified on colorectal cancer p53 protein IHC(Immunohistochemistry)image dataset.Dice value of the model reaches 0.8876 and classification accuracy reaches 90.28%.The results show that the proposed method can effectively grade the immunohistochemical images of colorectal cancer,and provide reliable auxiliary information for doctors to read the film.